
# loading the necessary libraries

import tensorflow as tf
from tensorflow import keras
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import matplotlib.patches as patches
import math
import pydicom as dcm

#Initialize the random number generator
import random as rd
rd.seed(0)
#Ignore the warnings
import warnings
warnings.filterwarnings("ignore")

import os
import cv2
from glob import glob

# from google.colab import drive  # import drive from google colab
# drive.mount('/content/drive',force_remount=True)  # default location for the drive
# os.chdir("/content/drive/My Drive/CV/CapstoneProject/") # we mount the google drive at /content/drive and change dir to this

from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Layer, Convolution2D, Flatten, Dense
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, GlobalAveragePooling2D
from tensorflow.keras.models import Model
import tensorflow.keras.utils as pltUtil
from tensorflow.keras.applications.mobilenet import preprocess_input

from keras.models import Sequential  # initial NN
from keras.layers import Dense, Dropout # construct each layer
from keras.layers import Conv2D # swipe across the image by 1
from keras.layers import MaxPooling2D # swipe across by pool size
from keras.layers import Flatten, GlobalAveragePooling2D,GlobalMaxPooling2D
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam

from keras.models import Sequential
from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout , BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
from keras.callbacks import ReduceLROnPlateau
from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization ,Conv2DTranspose
from tensorflow.keras.layers import UpSampling2D, Input, Concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.applications import MobileNet , VGG19
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from tensorflow.keras.metrics import Recall, Precision
from tensorflow.keras import backend as K
from PIL import Image
from numpy import asarray
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.layers import Concatenate, UpSampling2D, Conv2D, Reshape, GlobalAveragePooling2D


os.chdir("/Users/seenu/tensorflow/rsna-pneumonia-detection-challenge/")


# Reading the train labels dataset and showing first 5 records of this train labels
data= pd.read_csv ("stage_2_train_labels.csv")
data.head()


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30227 entries, 0 to 30226
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   patientId  30227 non-null  object 
 1   x          9555 non-null   float64
 2   y          9555 non-null   float64
 3   width      9555 non-null   float64
 4   height     9555 non-null   float64
 5   Target     30227 non-null  int64  
dtypes: float64(4), int64(1), object(1)
memory usage: 1.4+ MB


data.shape

(30227, 6)


# Detecting missing values

data.isnull().sum()

# below data reflects that 20672 chest x rays having null values and dont have any bounding boxes

patientId        0
x            20672
y            20672
width        20672
height       20672
Target           0
dtype: int64


# counting the Target those having chest X rays with out bounding boxes 

data[data.isnull().any(axis=1)].Target.value_counts()

0    20672
Name: Target, dtype: int64


# counting the Target those having chest X rays with bounding boxes

data[~data.isnull().any(axis=1)].Target.value_counts()

1    9555
Name: Target, dtype: int64


data[data.isnull().any(axis=1)]


# checking if there are unique values of patient ID

data["patientId"].is_unique

False


# checking the duplicate patient ids because corrdinates of bounding boxes might be duplicates

data["patientId"].duplicated().sum()

3543


duplicatept= data[data["patientId"].duplicated()]


duplicatept.head(5)


# checking the duplicate patient ID has how many bounding boxes

data[data.patientId=="00704310-78a8-4b38-8475-49f4573b2dbb"]


data[data.patientId=="00c0b293-48e7-4e16-ac76-9269ba535a62"]


# checking the count of Target column
data.Target.value_counts()

0    20672
1     9555
Name: Target, dtype: int64


# Reading the detailed class info

det_class= pd.read_csv(("stage_2_detailed_class_info.csv"))
det_class.head()


# checking the shape
det_class.shape

(30227, 2)


# checking the info
det_class.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30227 entries, 0 to 30226
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   patientId  30227 non-null  object
 1   class      30227 non-null  object
dtypes: object(2)
memory usage: 472.4+ KB


# checking the null values
det_class.isnull().sum()

patientId    0
class        0
dtype: int64


# checking the duplicate values

det_class.patientId.duplicated().sum()

3543


# checking the duplicate patients 
duplicate_class= det_class[det_class.patientId.duplicated()]
duplicate_class.head(5)


# checking the duplicate patients with their respective classes

det_class[det_class.patientId=="00704310-78a8-4b38-8475-49f4573b2dbb"]


det_class[det_class.patientId=="00c0b293-48e7-4e16-ac76-9269ba535a62"]


# checking the count of class column

det_class["class"].value_counts()

No Lung Opacity / Not Normal    11821
Lung Opacity                     9555
Normal                           8851
Name: class, dtype: int64


print('Lets check the distribution of `Target` and `class` column'); print('--'*40)
fig = plt.figure(figsize = (10, 6))
ax = fig.add_subplot(121)
g = (data['Target'].value_counts()
    .plot(kind = 'pie', autopct = '%.0f%%', 
          labels = ['Negative', 'Pneumonia Evidence'], 
          colors = ['violet', 'gainsboro'], 
          startangle = 90, 
          title = 'Distribution of Target', fontsize = 12)
    .set_ylabel(''))
ax = fig.add_subplot(122)
g = (det_class['class'].value_counts().sort_index(ascending = False)
    .plot(kind = 'pie', autopct = '%.0f%%', 
          colors = ['lightblue', 'lightgreen', 'brown'], 
          startangle = 90, title = 'Distribution of Class', 
          fontsize = 12)
    .set_ylabel(''))
plt.tight_layout()

Lets check the distribution of `Target` and `class` column
--------------------------------------------------------------------------------


print('Let\'s group by patient IDs and check number of bounding boxes for each unique patient ID');print('--'*44)
b_boxes = data[~data.isnull().any(axis=1)].groupby('patientId').size().to_frame('number_of_boxes').reset_index()

Let's group by patient IDs and check number of bounding boxes for each unique patient ID
----------------------------------------------------------------------------------------


b_boxes


data_df = b_boxes.merge(b_boxes, on = 'patientId', how = 'left')
print('Number of unique patient IDs in the dataset: {}'.format(len(b_boxes)))
print('\nNumber of patient IDs per b_boxes in the train dataset')
(b_boxes.groupby('number_of_boxes')
.size()
.to_frame('number_of_patientIDs_per_boxes')
.reset_index()
.set_index('number_of_boxes')
.sort_values(by = 'number_of_boxes'))

Number of unique patient IDs in the dataset: 6012

Number of patient IDs per b_boxes in the train dataset


#sorting both the datasets based on patientId
data.sort_values('patientId', inplace = True)
det_class.sort_values('patientId', inplace = True)


#concatenating the data labels and class labels file for model training
train_data = pd.concat([data, det_class['class']], axis = 1, sort = False)
print('The merged dataset has {} rows and {} columns and looks like:'.format(train_data.shape[0], train_data.shape[1]))
train_data.head()

The merged dataset has 30227 rows and 7 columns and looks like:


# analysing the stage 2 train images (DICOM format)
import pydicom as dcm
import math
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import matplotlib.patches as patches


def inspectImages(data):
    img_data = list(data.T.to_dict().values())
    f, ax = plt.subplots(3,3, figsize=(16,18))
    for i,data_row in enumerate(img_data):
        patientImage = data_row['patientId']
        dcm_file = 'stage_2_train_images/'+'{}.dcm'.format(patientImage)
        data_row_img_data = dcm.read_file(dcm_file)
        modality = data_row_img_data.Modality
        age = data_row_img_data.PatientAge
        sex = data_row_img_data.PatientSex
        data_row_img = dcm.dcmread(dcm_file)
        ax[i//3, i%3].imshow(data_row_img.pixel_array, cmap=plt.cm.bone) 
        ax[i//3, i%3].axis('off')
        ax[i//3, i%3].set_title('ID: {}\nModality: {} Age: {} Sex: {} Target: {}\nClass: {}\Bounds: {}:{}:{}:{}'.format(
                data_row['patientId'],
                modality, age, sex, data_row['Target'], data_row['class'], 
                data_row['x'],data_row['y'],data_row['width'],data_row['height']))
        label = data_row["class"]
        if not math.isnan(data_row['x']):
            x, y, width, height  =  data_row['x'],data_row['y'],data_row['width'],data_row['height']
            rect = patches.Rectangle((x, y),width, height,
                                 linewidth = 2,
                                 edgecolor = 'r',
                                 facecolor = 'none')

        # Draw the bounding box on top of the image
            ax[i//3, i%3].add_patch(rect)

    plt.show()


## checking few images which has pneumonia 

inspectImages(train_data[train_data['Target']==1].sample(9))


## checking few images which does not have pneumonia 
inspectImages(train_data[train_data['Target']==0].sample(9))


%%time
## DCIM image also being included with the meta data, 
## Function to read the dcim data and appending to the resultset
def readDCIMData(rowData):
    dcm_file = 'stage_2_train_images/'+'{}.dcm'.format(rowData.patientId)
    dcm_data = dcm.read_file(dcm_file)
    img = dcm_data.pixel_array
    return dcm_data.PatientSex,dcm_data.PatientAge

CPU times: user 3 µs, sys: 1 µs, total: 4 µs
Wall time: 7.15 µs


%%time
## Reading the image data and append it to the training_data dataset
train_data['sex'], train_data['age'] = zip(*train_data.apply(readDCIMData, axis=1))

CPU times: user 1min 34s, sys: 7.11 s, total: 1min 41s
Wall time: 1min 51s


train_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 30227 entries, 0 to 28988
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   patientId  30227 non-null  object 
 1   x          9555 non-null   float64
 2   y          9555 non-null   float64
 3   width      9555 non-null   float64
 4   height     9555 non-null   float64
 5   Target     30227 non-null  int64  
 6   class      30227 non-null  object 
 7   sex        30227 non-null  object 
 8   age        30227 non-null  object 
dtypes: float64(4), int64(1), object(4)
memory usage: 2.3+ MB


# Converting age to Numeric as the current data type is a String
train_data['age'] = train_data.age.astype(int)


train_data.describe(include="all")


## Distribution of Target

label_count=train_data['Target'].value_counts()
explode = (0.01,0.01)  

fig1, ax1 = plt.subplots(figsize=(4,4))
ax1.pie(label_count.values, labels=label_count.index, autopct='%1.1f%%', startangle=90)
ax1.axis('equal') 
plt.title( "Distribution of Target")
plt.show()


## Distribution of Classes

class_count=train_data['class'].value_counts()
explode = (0.01,0.01,0.01)  

fig1, ax1 = plt.subplots(figsize=(4,4))
ax1.pie(class_count.values, explode=explode, labels=class_count.index, autopct='%1.1f%%', 
        startangle=90)
ax1.axis('equal')
plt.title('Distribution of Class')
plt.show()


# insights: 
# there are 39% of patients with No Lung opacity , 
# 29.3% Normal and 
# the remaining are with Lung Opacity


# distribution of target and class

fig, ax = plt.subplots(nrows = 1, figsize = (8, 6))
temp = train_data.groupby('Target')['class'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'class', data = data_target_class, palette = 'tab10')
plt.title('Class and Target  Distrubution')

Text(0.5, 1.0, 'Class and Target  Distrubution')


# count of gender
train_data.sex.value_counts()

M    17216
F    13011
Name: sex, dtype: int64


# Distbution of Sex among the targets
fig, ax = plt.subplots(nrows = 1, figsize = (8, 5))
temp = train_data.groupby('Target')['sex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'Target', y = 'Values', hue = 'sex', data = data_target_class, palette = 'pastel')
plt.title('Sex and Target for Chest Exams')

Text(0.5, 1.0, 'Sex and Target for Chest Exams')


# # Distbution of Sex among the classes

fig, ax = plt.subplots(nrows = 1, figsize = (8, 5))
temp = train_data.groupby('class')['sex'].value_counts()
data_target_class = pd.DataFrame(data = {'Values': temp.values}, index = temp.index).reset_index()
sns.barplot(ax = ax, x = 'class', y = 'Values', hue = 'sex', data = data_target_class, palette = 'Set3')
plt.title('Sex and class for Chest Exams')

Text(0.5, 1.0, 'Sex and class for Chest Exams')


# plot of age distribution
# dist plot
sns.distplot(train_data.age)

<AxesSubplot:xlabel='age', ylabel='Density'>


# Distribution of PatientAge who have pneumonia

sns.distplot(train_data.loc[train_data['Target'] == 1, 'age'])

<AxesSubplot:xlabel='age', ylabel='Density'>


# Subplots

fig = plt.figure(figsize = (12, 4))

ax = fig.add_subplot(121)
g = (sns.distplot(train_data['age']).set_title('Distribution of PatientAge'))

ax = fig.add_subplot(122)
g = (sns.distplot(train_data.loc[train_data['Target'] == 1, 'age']).set_title('Distribution of PatientAge who have pneumonia'))


# distribution of Age among target

# Bar plot

sns.barplot(x='Target', y='age', data=train_data)

<AxesSubplot:xlabel='Target', ylabel='age'>


# Distribution of Age among class

# Bar plot
 
sns.barplot(x='class', y='age', data=train_data)

<AxesSubplot:xlabel='class', ylabel='age'>


# Box plot

plt.figure(figsize= (10,5))
sns.boxplot (x= "class", y= "age", data= train_data)

<AxesSubplot:xlabel='class', ylabel='age'>


# Insight of box plot:
# The class which has no pneuomia has few outliers , their age is somewhere around 150 years


# corrrelation matrix
corr_mat = train_data.corr()
plt.figure(figsize=(12,5))

sns.heatmap(corr_mat,annot=True)

<AxesSubplot:>


# Prepare data for Modelling:-


# We will convert the dataset to have just two classes for our ease of doing. 
# Going forward Target 0 will correspond to Normal class whereas Target 1 will corresponds with Lung Opacity.


train_data_df = train_data.copy()


#convert the dataset into two classes only:
train_data_df['class'].replace('No Lung Opacity / Not Normal', 'Normal', inplace = True)
print('The merge dataset now looks like:')
train_data_df.head()

The merge dataset now looks like:


# setting all NaN values to 0 in the train_data_df datasets
# x, y, width and hight values as zero(0) means no bounding box.
train_data_df.fillna(0, inplace = True)
print('The training data now looks as: \n')
train_data_df.head()

The training data now looks as:


#convert the dataset into two classes only:
train_data_df['class'].replace('No Lung Opacity / Not Normal', 'Normal', inplace = True)
print('The merge dataset now looks like:')
train_data_df.head()

The merge dataset now looks like:


train_data_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 30227 entries, 0 to 28988
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   patientId  30227 non-null  object 
 1   x          30227 non-null  float64
 2   y          30227 non-null  float64
 3   width      30227 non-null  float64
 4   height     30227 non-null  float64
 5   Target     30227 non-null  int64  
 6   class      30227 non-null  object 
 7   sex        30227 non-null  object 
 8   age        30227 non-null  int64  
dtypes: float64(4), int64(2), object(3)
memory usage: 2.3+ MB


train_data_df['class'].value_counts()

Normal          20672
Lung Opacity     9555
Name: class, dtype: int64


train_sample = train_data_df.copy


train_data_df.head()


train_data_df['class'].value_counts()

Normal          20672
Lung Opacity     9555
Name: class, dtype: int64


## Let's visualize a handful of positive and negative examples. 
## The function below plots five images.

def plot_five_images(images):
  """
  As the name implies, plots five images
  in a single row

  Args:
    images: list or np.array containing images

  Returns: None
  """
  # Establish an image index
  a = 1

  # Instantiate the plot
  fig = plt.figure(figsize=(15,15))

  # Plot the images
  for image in images:
    plt.subplot(1, 5, a)
    plt.imshow(image)
    plt.axis('off')
    a += 1
  plt.show()


#model loss
def display_loss_accuracy(x):
    '''Takes in the models history and returns a plot of the history of the log loss and the accuracy'''
    
    plt.figure(figsize=(12,4))

    plt.subplot(1,2,1)
    plt.plot(x.history['loss'])
    plt.plot(x.history['val_loss'])
    plt.title('Model Loss')
    plt.ylabel('Loss')
    plt.xlabel('Epoch')
    plt.legend(['Training Loss', 'Validation Loss'], loc='upper left')
    plt.grid(True)
    
    plt.subplot(1,2,2)
    plt.plot(x.history['accuracy'])
    plt.plot(x.history['val_accuracy'])
    plt.title('Model Accuracy')
    plt.ylabel('Accuracy')
    plt.xlabel('Epoch')
    plt.legend(['Training Accuracy', 'Test Accuracy'], loc='upper left')
    plt.grid(True)
    plt.tight_layout()
    
    return plt.show()


from sklearn.metrics import f1_score, roc_curve, roc_auc_score, confusion_matrix, classification_report
from sklearn.metrics import ConfusionMatrixDisplay

def evaluate_model(model, test_x, test_y, threshold=0.5):
    """
  Applies a series of evaluation tools to generate
  a report of the model's performance.

  Args:
    model: tensorflow.python.keras.engine.training.Model
           instance trained on data

    test_x: np.array representing the test set

    test_y: np.array of test labels

    threshold: float giving the binary prediction threshold

  Returns:
    None, but displays evaluation output
    """

   # Predict from the test set
    y_probs = model.predict(x=X_test)
    y_probs = y_probs.reshape((len(y_probs),))

    # Convert to binary probabilities
    y_hat   = y_probs > threshold
    y_hat   = y_hat.reshape(len(y_hat),)

    # Plot the confusion matrix
    print('----------------------Confusion Matrix---------------------\n')

    cm = ConfusionMatrixDisplay.from_predictions(y_test, y_hat,cmap='viridis')
#    print(cm)
    plt.show(cm)

    # Print the classification report
    print('\n\n-----------------Classification Report-----------------\n')
    print(classification_report(y_test, y_hat))

    # Display the ROC curve
    print('\n\n-----------------------ROC Curve-----------------------\n')
    fpr, tpr, thresholds = roc_curve(test_y, y_probs)
    roc_auc = roc_auc_score(y_test, y_probs)
    plt.figure()
    lw = 2
    plt.plot(fpr, tpr, color='darkorange',
           lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()


images = []
ADJUSTED_IMAGE_SIZE = 128
imageList = []
classLabels = []
labels = []
originalImage = []
# Function to read the image from the path and reshape the image to size
def readAndReshapeImage(image):
    img = np.array(image).astype(np.uint8)
    ## Resize the image
    res = cv2.resize(img,(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
    return res

## Read the imahge and resize the image
def populateImage(rowData):
    for index, row in rowData.iterrows():
        patientId = row.patientId
        classlabel = row["class"]
        dcm_file = 'stage_2_train_images/'+'{}.dcm'.format(patientId)
        dcm_data = dcm.read_file(dcm_file)
        img = dcm_data.pixel_array
        ## Converting the image to 3 channels as the dicom image pixel does not have colour classes with it
        if len(img.shape) != 3 or img.shape[2] != 3:
            img = np.stack((img,) * 3, -1)
        imageList.append(readAndReshapeImage(img))
#         originalImage.append(img)
        classLabels.append(classlabel)
    tmpImages = np.array(imageList)
    tmpLabels = np.array(classLabels)
#     originalImages = np.array(originalImage)
    return tmpImages,tmpLabels


%%time
# Reading the images into numpy array
images,labels = populateImage(train_data_df)

CPU times: user 2min 9s, sys: 2.59 s, total: 2min 12s
Wall time: 2min 12s


images.shape , labels.shape

((30227, 128, 128, 3), (30227,))


## Checking one of the converted image 
plt.imshow(images[1200])

<matplotlib.image.AxesImage at 0x17e1b0ee0>


## check the unique labels
np.unique(labels),len(np.unique(labels))

(array(['Lung Opacity', 'Normal'], dtype='<U12'), 2)


## encoding the labels
from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
label = lb.fit_transform(labels)


## splitting into train ,test and validation data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(images, label, test_size=0.2,shuffle = True, random_state=22, stratify=label)
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25,shuffle = True, random_state=22, stratify=y_train)

X_train = X_train/255
X_test = X_test/255
X_val = X_val/255


values, counts = np.unique(y_train, return_counts=True)
values1, counts1 = np.unique(y_test, return_counts=True)
values2, counts2 = np.unique(y_val, return_counts=True)
print("Train stratify label:",values)
print("Train stratify label counts:",counts)
print("Test stratify label:",values1)
print("Test stratify label counts:",counts1)
print("Validation stratify label:",values2)
print("Validation stratify label counts:",counts2)

Train stratify label: [0 1]
Train stratify label counts: [ 5733 12402]
Test stratify label: [0 1]
Test stratify label counts: [1911 4135]
Validation stratify label: [0 1]
Validation stratify label counts: [1911 4135]


percent_positive1 = y_train.sum() / len(y_train)
print('Percent of train images that are positive - %.3f' %percent_positive1)

percent_positive2 = y_test.sum() / len(y_test)
print('Percent of test images that are positive - %.3f' %percent_positive2)

percent_positive3 = y_val.sum() / len(y_val)
print('Percent of test images that are positive - %.3f' %percent_positive3)

Percent of train images that are positive - 0.684
Percent of test images that are positive - 0.684
Percent of test images that are positive - 0.684


## Function to create a dataframe for results
def createResult(name,accuracy,testscore):
    result = pd.DataFrame({'Method':[name], 'Accuracy': [accuracy] ,'Test Score':[testscore]})
    return result


%%time
# Basic model
batch_size = 100
epochs = 30

random_state=22

model1= Sequential()
model1.add(Conv2D(32,(3,3),input_shape=(128,128,3),activation='relu'))
model1.add(MaxPooling2D(2,2))
    
model1.add(Conv2D(64,(3,3),activation='relu'))
model1.add(MaxPooling2D(2,2))
model1.add(Dropout(0.5))
    
model1.add(Conv2D(128,(3,3),activation='relu'))
model1.add(MaxPooling2D(2,2))
model1.add(Dropout(0.5))
    
model1.add(Conv2D(256,(3,3),activation='relu'))
model1.add(MaxPooling2D(2,2))
    
model1.add(Flatten())
model1.add(Dropout(0.5))
model1.add(Dense(256,activation='relu'))
model1.add(Dense(1,activation='sigmoid'))

# compiling the model

model1.compile(optimizer=Adam(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])

# Fitting the model

history1 = model1.fit(X_train, 
                  y_train, 
                  epochs = epochs, 
                  validation_data =(X_val,y_val),
                  batch_size = batch_size,verbose = 1)

Metal device set to: Apple M1 Pro

2022-08-26 12:02:34.056820: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-08-26 12:02:34.057011: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)

Epoch 1/30

2022-08-26 12:02:53.406589: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-08-26 12:02:53.964874: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.5875 - accuracy: 0.6953

2022-08-26 12:03:15.270826: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - 24s 125ms/step - loss: 0.5875 - accuracy: 0.6953 - val_loss: 0.5039 - val_accuracy: 0.7620
Epoch 2/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4975 - accuracy: 0.7591 - val_loss: 0.4883 - val_accuracy: 0.7721
Epoch 3/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4818 - accuracy: 0.7690 - val_loss: 0.4715 - val_accuracy: 0.7746
Epoch 4/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4766 - accuracy: 0.7766 - val_loss: 0.4789 - val_accuracy: 0.7747
Epoch 5/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4704 - accuracy: 0.7787 - val_loss: 0.4618 - val_accuracy: 0.7785
Epoch 6/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4668 - accuracy: 0.7785 - val_loss: 0.4565 - val_accuracy: 0.7775
Epoch 7/30
182/182 [==============================] - 18s 100ms/step - loss: 0.4641 - accuracy: 0.7839 - val_loss: 0.4595 - val_accuracy: 0.7807
Epoch 8/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4570 - accuracy: 0.7889 - val_loss: 0.4692 - val_accuracy: 0.7828
Epoch 9/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4526 - accuracy: 0.7920 - val_loss: 0.4471 - val_accuracy: 0.7873
Epoch 10/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4461 - accuracy: 0.7946 - val_loss: 0.4491 - val_accuracy: 0.7851
Epoch 11/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4422 - accuracy: 0.7964 - val_loss: 0.4422 - val_accuracy: 0.7870
Epoch 12/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4405 - accuracy: 0.7971 - val_loss: 0.4398 - val_accuracy: 0.7918
Epoch 13/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4333 - accuracy: 0.8017 - val_loss: 0.4312 - val_accuracy: 0.7946
Epoch 14/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4351 - accuracy: 0.8006 - val_loss: 0.4371 - val_accuracy: 0.7944
Epoch 15/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4255 - accuracy: 0.8033 - val_loss: 0.4291 - val_accuracy: 0.7972
Epoch 16/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4237 - accuracy: 0.8050 - val_loss: 0.4304 - val_accuracy: 0.7989
Epoch 17/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4213 - accuracy: 0.8096 - val_loss: 0.4224 - val_accuracy: 0.8043
Epoch 18/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4168 - accuracy: 0.8049 - val_loss: 0.4213 - val_accuracy: 0.8038
Epoch 19/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4144 - accuracy: 0.8076 - val_loss: 0.4218 - val_accuracy: 0.8023
Epoch 20/30
182/182 [==============================] - 18s 100ms/step - loss: 0.4103 - accuracy: 0.8104 - val_loss: 0.4261 - val_accuracy: 0.8019
Epoch 21/30
182/182 [==============================] - 18s 99ms/step - loss: 0.4029 - accuracy: 0.8165 - val_loss: 0.4219 - val_accuracy: 0.8055
Epoch 22/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3984 - accuracy: 0.8159 - val_loss: 0.4214 - val_accuracy: 0.7966
Epoch 23/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3993 - accuracy: 0.8149 - val_loss: 0.4150 - val_accuracy: 0.8078
Epoch 24/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3901 - accuracy: 0.8200 - val_loss: 0.4121 - val_accuracy: 0.8081
Epoch 25/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3841 - accuracy: 0.8239 - val_loss: 0.4137 - val_accuracy: 0.8086
Epoch 26/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3814 - accuracy: 0.8280 - val_loss: 0.4329 - val_accuracy: 0.8062
Epoch 27/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3820 - accuracy: 0.8250 - val_loss: 0.4131 - val_accuracy: 0.8114
Epoch 28/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3703 - accuracy: 0.8319 - val_loss: 0.4142 - val_accuracy: 0.8119
Epoch 29/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3712 - accuracy: 0.8314 - val_loss: 0.4234 - val_accuracy: 0.8073
Epoch 30/30
182/182 [==============================] - 18s 99ms/step - loss: 0.3654 - accuracy: 0.8345 - val_loss: 0.4161 - val_accuracy: 0.8114
CPU times: user 1min 19s, sys: 6min 47s, total: 8min 6s
Wall time: 9min 27s


# Visualisation of training and validation accuracy Vs their loss
train1_acc = history1.history['accuracy']
display_loss_accuracy(history1)


# Insights of basic model with 2 classes:

# there has been dip in the traning loss but validation loss has no much significant dip in loss
# Validation accuracy went up to 81% approx
# while training accuracy was 83% and test accuracy was 81%


# Evaluating the accuracy
model1_test_loss, model1_test_acc = model1.evaluate(X_test, y_test, verbose=1)
print('Test loss:', model1_test_loss)
print('Test accuracy:',model1_test_acc)

189/189 [==============================] - 5s 24ms/step - loss: 0.4157 - accuracy: 0.8134
Test loss: 0.41569533944129944
Test accuracy: 0.8134303689002991


resultDF1 = createResult("CNN model with 2 classes"
                         ,round(train1_acc[-1]*100,2),round(model1_test_acc*100,2))
resultDF1


evaluate_model(model1, X_test, y_test)

  3/189 [..............................] - ETA: 4s

2022-08-26 12:15:34.436661: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 4s 20ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.73      0.66      0.69      1911
           1       0.85      0.89      0.87      4135

    accuracy                           0.81      6046
   macro avg       0.79      0.77      0.78      6046
weighted avg       0.81      0.81      0.81      6046


-----------------------ROC Curve-----------------------


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob1 = model1.predict(x=X_test)
y_prob1 = y_prob1.reshape((len(y_prob1),))

    # Convert to binary probabilities
y_hat1   = y_prob1 > threshold
y_hat1   = y_hat1.reshape(len(y_hat1),)

    
reportData = classification_report(y_test, y_hat1,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF1[data+"_"+subData] = reportData[data][subData]

resultDF1['1_precision'] =round(resultDF1['1_precision']*100,2)
resultDF1['1_recall'] =round(resultDF1['1_recall']*100,2)
resultDF1['1_f1-score'] =round(resultDF1['1_f1-score']*100,2)
resultDF1

189/189 [==============================] - 4s 20ms/step


 train_data.head()


# making a copy of the train data
sample_traindata = train_data.copy()


## Checking the training data set with class distbution 
sample_traindata["class"].value_counts()

No Lung Opacity / Not Normal    11821
Lung Opacity                     9555
Normal                           8851
Name: class, dtype: int64


sample_traindata.head()


sample_traindata.fillna(0, inplace = True)
print('The training data now looks as: \n')
sample_traindata.head()

The training data now looks as:


sampleimages = []
ADJUSTED_IMAGE_SIZE = 128
imageList = []
classLabels = []
samplelabels = []
originalImage = []
# Function to read the image from the path and reshape the image to size
def readAndReshapeImage(image):
    img = np.array(image).astype(np.uint8)
    ## Resize the image
    res = cv2.resize(img,(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
    return res

## Read the imahge and resize the image
def populateImage(rowData):
    for index, row in rowData.iterrows():
        patientId = row.patientId
        classlabel = row["class"]
        dcm_file = 'stage_2_train_images/'+'{}.dcm'.format(patientId)
        dcm_data = dcm.read_file(dcm_file)
        img = dcm_data.pixel_array
        ## Converting the image to 3 channels as the dicom image pixel does not have colour classes wiht it
        if len(img.shape) != 3 or img.shape[2] != 3:
            img = np.stack((img,) * 3, -1)
        imageList.append(readAndReshapeImage(img))
#         originalImage.append(img)
        classLabels.append(classlabel)
    tmpImages = np.array(imageList)
    tmpLabels = np.array(classLabels)
#     originalImages = np.array(originalImage)
    return tmpImages,tmpLabels


%%time
sampleimages,samplelabels = populateImage(sample_traindata)

CPU times: user 2min 12s, sys: 7.69 s, total: 2min 20s
Wall time: 2min 30s


sampleimages.shape , samplelabels.shape

((30227, 128, 128, 3), (30227,))


## Checking one of the converted image 
plt.imshow(sampleimages[1200])

<matplotlib.image.AxesImage at 0x29f532e50>


## check the unique labels
np.unique(samplelabels),len(np.unique(samplelabels))

(array(['Lung Opacity', 'No Lung Opacity / Not Normal', 'Normal'],
       dtype='<U28'),
 3)


## encoding the labels
from sklearn.preprocessing import LabelBinarizer
lb = LabelBinarizer()
ysample = lb.fit_transform(samplelabels)


# Train test split for 3 class dataset

X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(sampleimages, ysample, test_size=0.2, shuffle = True,random_state=22, stratify=ysample)
X_train_c, X_val_c, y_train_c, y_val_c = train_test_split(X_train_c, y_train_c, test_size=0.25, shuffle = True,random_state=22 ,stratify=y_train_c)

X_train_c = X_train_c/255
X_test_c = X_test_c/255
X_val_c = X_val_c/255


%%time
# Basic CNN model: used 32 filters with kernel size (3, 3) followed by 64, 128 and 256 filters with same kernel size
# with maximum pooling and drop outs
# finally used softmax activation layer
random_state=22

model2= Sequential()
model2.add(Conv2D(32,(3,3),input_shape=(128,128,3),activation='relu'))
model2.add(MaxPooling2D(2,2))
    
model2.add(Conv2D(64,(3,3),activation='relu'))
model2.add(MaxPooling2D(2,2))
model2.add(Dropout(0.5))
    
model2.add(Conv2D(128,(3,3),activation='relu'))
model2.add(MaxPooling2D(2,2))
model2.add(Dropout(0.5))
    
model2.add(Conv2D(256,(3,3),activation='relu'))
model2.add(MaxPooling2D(2,2))
    
model2.add(Flatten())
model2.add(Dropout(0.5))
model2.add(Dense(256,activation='relu'))
model2.add(Dense(3,activation='softmax'))

# compiling the model

model2.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])

# Fitting the model

history2 = model2.fit(X_train_c, 
                  y_train_c, 
                  epochs = epochs, 
                  validation_data =(X_val_c,y_val_c),
                  batch_size = batch_size,verbose = 1)

Epoch 1/30

2022-08-26 12:20:54.502535: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 1.0071 - accuracy: 0.4710

2022-08-26 12:21:23.014069: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - 33s 177ms/step - loss: 1.0071 - accuracy: 0.4710 - val_loss: 0.9320 - val_accuracy: 0.5471
Epoch 2/30
182/182 [==============================] - 27s 147ms/step - loss: 0.8900 - accuracy: 0.5642 - val_loss: 0.8931 - val_accuracy: 0.5814
Epoch 3/30
182/182 [==============================] - 27s 148ms/step - loss: 0.8630 - accuracy: 0.5834 - val_loss: 0.8813 - val_accuracy: 0.5657
Epoch 4/30
182/182 [==============================] - 27s 146ms/step - loss: 0.8460 - accuracy: 0.5915 - val_loss: 0.8713 - val_accuracy: 0.5890
Epoch 5/30
182/182 [==============================] - 134s 738ms/step - loss: 0.8338 - accuracy: 0.5965 - val_loss: 0.8689 - val_accuracy: 0.5736
Epoch 6/30
182/182 [==============================] - 27s 146ms/step - loss: 0.8175 - accuracy: 0.6124 - val_loss: 0.8391 - val_accuracy: 0.6030
Epoch 7/30
182/182 [==============================] - 27s 146ms/step - loss: 0.8025 - accuracy: 0.6192 - val_loss: 0.8298 - val_accuracy: 0.6016
Epoch 8/30
182/182 [==============================] - 26s 145ms/step - loss: 0.7895 - accuracy: 0.6226 - val_loss: 0.8101 - val_accuracy: 0.6080
Epoch 9/30
182/182 [==============================] - 27s 147ms/step - loss: 0.7805 - accuracy: 0.6293 - val_loss: 0.8112 - val_accuracy: 0.6095
Epoch 10/30
182/182 [==============================] - 26s 145ms/step - loss: 0.7708 - accuracy: 0.6349 - val_loss: 0.8110 - val_accuracy: 0.6115
Epoch 11/30
182/182 [==============================] - 27s 146ms/step - loss: 0.7585 - accuracy: 0.6363 - val_loss: 0.7706 - val_accuracy: 0.6305
Epoch 12/30
182/182 [==============================] - 26s 145ms/step - loss: 0.7563 - accuracy: 0.6413 - val_loss: 0.7735 - val_accuracy: 0.6232
Epoch 13/30
182/182 [==============================] - 27s 146ms/step - loss: 0.7495 - accuracy: 0.6469 - val_loss: 0.7889 - val_accuracy: 0.6141
Epoch 14/30
182/182 [==============================] - 27s 146ms/step - loss: 0.7398 - accuracy: 0.6522 - val_loss: 0.7727 - val_accuracy: 0.6318
Epoch 15/30
182/182 [==============================] - 27s 146ms/step - loss: 0.7354 - accuracy: 0.6582 - val_loss: 0.7838 - val_accuracy: 0.6234
Epoch 16/30
182/182 [==============================] - 26s 146ms/step - loss: 0.7274 - accuracy: 0.6550 - val_loss: 0.7442 - val_accuracy: 0.6457
Epoch 17/30
182/182 [==============================] - 27s 147ms/step - loss: 0.7238 - accuracy: 0.6613 - val_loss: 0.7441 - val_accuracy: 0.6452
Epoch 18/30
182/182 [==============================] - 27s 146ms/step - loss: 0.7187 - accuracy: 0.6631 - val_loss: 0.8054 - val_accuracy: 0.6092
Epoch 19/30
182/182 [==============================] - 27s 147ms/step - loss: 0.7138 - accuracy: 0.6676 - val_loss: 0.7754 - val_accuracy: 0.6254
Epoch 20/30
182/182 [==============================] - 26s 146ms/step - loss: 0.7021 - accuracy: 0.6726 - val_loss: 0.7482 - val_accuracy: 0.6456
Epoch 21/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6954 - accuracy: 0.6755 - val_loss: 0.7486 - val_accuracy: 0.6399
Epoch 22/30
182/182 [==============================] - 102s 563ms/step - loss: 0.6958 - accuracy: 0.6739 - val_loss: 0.7579 - val_accuracy: 0.6376
Epoch 23/30
182/182 [==============================] - 27s 147ms/step - loss: 0.6894 - accuracy: 0.6797 - val_loss: 0.7575 - val_accuracy: 0.6399
Epoch 24/30
182/182 [==============================] - 26s 146ms/step - loss: 0.6787 - accuracy: 0.6874 - val_loss: 0.7517 - val_accuracy: 0.6409
Epoch 25/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6734 - accuracy: 0.6911 - val_loss: 0.7716 - val_accuracy: 0.6307
Epoch 26/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6699 - accuracy: 0.6919 - val_loss: 0.7299 - val_accuracy: 0.6492
Epoch 27/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6619 - accuracy: 0.6980 - val_loss: 0.7250 - val_accuracy: 0.6551
Epoch 28/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6637 - accuracy: 0.6945 - val_loss: 0.7612 - val_accuracy: 0.6393
Epoch 29/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6519 - accuracy: 0.7055 - val_loss: 0.7348 - val_accuracy: 0.6555
Epoch 30/30
182/182 [==============================] - 27s 146ms/step - loss: 0.6482 - accuracy: 0.7006 - val_loss: 0.7376 - val_accuracy: 0.6489
CPU times: user 1min 35s, sys: 12min 45s, total: 14min 21s
Wall time: 16min 45s


## Plotting the accuracy vs loss graph
train2_acc = history2.history['accuracy']
display_loss_accuracy(history2)


## evaluating the acuracy
model2_test_loss, model2_test_acc = model2.evaluate(X_test_c, y_test_c)
print('Test loss:', model2_test_loss)
print('Test accuracy:', model2_test_acc)

189/189 [==============================] - 5s 26ms/step - loss: 0.7521 - accuracy: 0.6480
Test loss: 0.7521150708198547
Test accuracy: 0.6480317711830139


resultDF2 = createResult("CNN model with 3 classes"
                         ,round(train2_acc[-1]*100,2),round(model2_test_acc*100,2))
resultDF2


# confusion matrix

from sklearn.metrics import confusion_matrix
import itertools
#Class 0 ,1 and 2:
#Class 0 is Lung Opacity
#Class 1 is No Lung Opacity/Normal
#Class 2 is Normal

plt.subplots(figsize=(22,7)) #setting the size of the plot 

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

# Predicting the values from the validation dataset
y_pred_3c = model2.predict(X_test_c)
# Converting predictions classes to one hot vectors 
y_pred_classes = np.argmax(y_pred_3c,axis = 1) 
# Convert validation observations to one hot vectors
y_true_c = np.argmax(y_test_c,axis = 1) 
# computing the confusion matrix
confusion_mtx = confusion_matrix(y_true_c, y_pred_classes) 
# plotting the confusion matrix
plot_confusion_matrix(confusion_mtx, classes = range(3))


# Print the classification report
print('\n\n-----------------Classification Report-----------------\n')
print(classification_report(y_true_c, y_pred_classes))

  1/189 [..............................] - ETA: 50s

2022-08-26 12:40:26.863683: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 4s 21ms/step


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.69      0.66      0.68      1911
           1       0.61      0.48      0.54      2365
           2       0.65      0.86      0.74      1770

    accuracy                           0.65      6046
   macro avg       0.65      0.67      0.65      6046
weighted avg       0.65      0.65      0.64      6046


# the model has predicted mostly wrong in this case to the Target 0. Type 2 error


# classification report

from sklearn.metrics import recall_score, confusion_matrix, precision_score, f1_score, accuracy_score, roc_auc_score,classification_report
from sklearn.metrics import classification_report


reportData = classification_report(y_true_c, y_pred_classes,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF2[data+"_"+subData] = reportData[data][subData]
resultDF2['1_precision'] =round(resultDF2['1_precision']*100,2)
resultDF2['1_recall'] =round(resultDF2['1_recall']*100,2)
resultDF2['1_f1-score'] =round(resultDF2['1_f1-score']*100,2)
resultDF2


train_gen = tf.keras.preprocessing.image.ImageDataGenerator(
#    rescale=1/255,
    horizontal_flip=True,
    rotation_range=40,
    shear_range=0.25,
    zoom_range=0.2)

#train_gen = tf.keras.preprocessing.image.ImageDataGenerator(
#    rotation_range = 40,  # randomly rotate images in the range (degrees, 0 to 180)
#    zoom_range = 0.2, # Randomly zoom image 
#    shear_range=0.25
#    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
#    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
#    horizontal_flip = True,  # randomly flip images
#    vertical_flip=False
#)

valid_gen  = tf.keras.preprocessing.image.ImageDataGenerator()
  
                                    
train_generator = train_gen.flow(
    X_train,
    y_train,
    batch_size=batch_size)
  
valid_generator = valid_gen.flow(
    X_val,
    y_val,
    batch_size=batch_size)


# Plotting the augmented images gives a sense of how the images are manipulated.
X_plot, y_plot = next(train_generator)
plot_five_images(X_plot[:5])


# We are going to train the model-1 with data augumentation images.


%%time
random_state=22

# Basic model
model3= Sequential()
model3.add(Conv2D(32,(3,3),input_shape=(128,128,3),activation='relu'))
model3.add(MaxPooling2D(2,2))
    
model3.add(Conv2D(64,(3,3),activation='relu'))
model3.add(MaxPooling2D(2,2))
model3.add(Dropout(0.2))
    
model3.add(Conv2D(128,(3,3),activation='relu'))
model3.add(MaxPooling2D(2,2))
model3.add(Dropout(0.2))
    
model3.add(Conv2D(256,(3,3),activation='relu'))
model3.add(MaxPooling2D(2,2))
    
model3.add(Flatten())
model3.add(Dropout(0.2))
model3.add(Dense(256,activation='relu'))
model3.add(Dense(1,activation='sigmoid'))

# compiling the model

model3.compile(optimizer=Adam(lr=0.001),loss='binary_crossentropy',metrics=['accuracy'])

# Fitting the model

history3 = model3.fit(x=train_generator,
                  epochs = epochs, 
                  validation_data=valid_generator,
                  batch_size = batch_size,
                  verbose = 1)

Epoch 1/30

2022-08-26 12:41:09.779032: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.5641 - accuracy: 0.7159

2022-08-26 12:41:43.163466: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - 36s 189ms/step - loss: 0.5641 - accuracy: 0.7159 - val_loss: 0.4940 - val_accuracy: 0.7572
Epoch 2/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4964 - accuracy: 0.7606 - val_loss: 0.4781 - val_accuracy: 0.7717
Epoch 3/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4874 - accuracy: 0.7696 - val_loss: 0.4925 - val_accuracy: 0.7509
Epoch 4/30
182/182 [==============================] - 31s 169ms/step - loss: 0.4865 - accuracy: 0.7721 - val_loss: 0.4756 - val_accuracy: 0.7736
Epoch 5/30
182/182 [==============================] - 60s 332ms/step - loss: 0.4791 - accuracy: 0.7717 - val_loss: 0.4715 - val_accuracy: 0.7746
Epoch 6/30
182/182 [==============================] - 31s 170ms/step - loss: 0.4761 - accuracy: 0.7760 - val_loss: 0.4722 - val_accuracy: 0.7732
Epoch 7/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4732 - accuracy: 0.7793 - val_loss: 0.4695 - val_accuracy: 0.7784
Epoch 8/30
182/182 [==============================] - 31s 169ms/step - loss: 0.4733 - accuracy: 0.7796 - val_loss: 0.4714 - val_accuracy: 0.7794
Epoch 9/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4726 - accuracy: 0.7768 - val_loss: 0.4700 - val_accuracy: 0.7779
Epoch 10/30
182/182 [==============================] - 31s 169ms/step - loss: 0.4683 - accuracy: 0.7819 - val_loss: 0.4582 - val_accuracy: 0.7804
Epoch 11/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4641 - accuracy: 0.7836 - val_loss: 0.4784 - val_accuracy: 0.7787
Epoch 12/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4616 - accuracy: 0.7846 - val_loss: 0.4596 - val_accuracy: 0.7772
Epoch 13/30
182/182 [==============================] - 31s 167ms/step - loss: 0.4636 - accuracy: 0.7833 - val_loss: 0.4552 - val_accuracy: 0.7827
Epoch 14/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4617 - accuracy: 0.7847 - val_loss: 0.4502 - val_accuracy: 0.7865
Epoch 15/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4577 - accuracy: 0.7878 - val_loss: 0.4423 - val_accuracy: 0.7875
Epoch 16/30
182/182 [==============================] - 31s 170ms/step - loss: 0.4563 - accuracy: 0.7862 - val_loss: 0.4508 - val_accuracy: 0.7853
Epoch 17/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4544 - accuracy: 0.7892 - val_loss: 0.4493 - val_accuracy: 0.7868
Epoch 18/30
182/182 [==============================] - 31s 169ms/step - loss: 0.4513 - accuracy: 0.7896 - val_loss: 0.4449 - val_accuracy: 0.7883
Epoch 19/30
182/182 [==============================] - 31s 173ms/step - loss: 0.4493 - accuracy: 0.7914 - val_loss: 0.4381 - val_accuracy: 0.7942
Epoch 20/30
182/182 [==============================] - 32s 174ms/step - loss: 0.4483 - accuracy: 0.7916 - val_loss: 0.4403 - val_accuracy: 0.7926
Epoch 21/30
182/182 [==============================] - 31s 170ms/step - loss: 0.4483 - accuracy: 0.7929 - val_loss: 0.4393 - val_accuracy: 0.7908
Epoch 22/30
182/182 [==============================] - 31s 171ms/step - loss: 0.4479 - accuracy: 0.7928 - val_loss: 0.4442 - val_accuracy: 0.7903
Epoch 23/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4435 - accuracy: 0.7937 - val_loss: 0.4397 - val_accuracy: 0.7903
Epoch 24/30
182/182 [==============================] - 32s 175ms/step - loss: 0.4445 - accuracy: 0.7948 - val_loss: 0.4366 - val_accuracy: 0.7987
Epoch 25/30
182/182 [==============================] - 32s 174ms/step - loss: 0.4414 - accuracy: 0.7954 - val_loss: 0.4454 - val_accuracy: 0.7914
Epoch 26/30
182/182 [==============================] - 32s 175ms/step - loss: 0.4426 - accuracy: 0.7948 - val_loss: 0.4304 - val_accuracy: 0.7994
Epoch 27/30
182/182 [==============================] - 31s 168ms/step - loss: 0.4391 - accuracy: 0.7954 - val_loss: 0.4393 - val_accuracy: 0.7911
Epoch 28/30
182/182 [==============================] - 31s 170ms/step - loss: 0.4394 - accuracy: 0.7951 - val_loss: 0.4316 - val_accuracy: 0.7962
Epoch 29/30
182/182 [==============================] - 31s 170ms/step - loss: 0.4400 - accuracy: 0.7969 - val_loss: 0.4284 - val_accuracy: 0.7982
Epoch 30/30
182/182 [==============================] - 32s 177ms/step - loss: 0.4397 - accuracy: 0.7972 - val_loss: 0.4299 - val_accuracy: 0.7984
CPU times: user 14min 54s, sys: 2min 25s, total: 17min 19s
Wall time: 16min 6s


# Evaluating the acuracy , we have got accuracy of 80% for both train and test.
model3_test_loss, model3_test_acc = model3.evaluate(X_test, y_test, verbose=1)
print('Test Loss of the model is -:', model3_test_loss)
print('Test Accuracy of the model is:', model3_test_acc)

189/189 [==============================] - 3s 13ms/step - loss: 0.4289 - accuracy: 0.8028
Test Loss of the model is -: 0.4288730323314667
Test Accuracy of the model is: 0.8028448820114136


## Plottting the accuracy vs loss graph
train3_acc = history3.history['accuracy']
display_loss_accuracy(history3)


# printing the dataframe
# printing the dataframe
resultDF3 = createResult("CNN model-1 with Augumentation"
                         ,round(train3_acc[-1]*100,2),round(model3_test_acc*100,2))
resultDF3


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob3 = model3.predict(x=X_test)
y_prob3 = y_prob3.reshape((len(y_prob3),))

    # Convert to binary probabilities
y_hat3   = y_prob3 > threshold
y_hat3   = y_hat3.reshape(len(y_hat3),)

    
reportData = classification_report(y_test, y_hat3,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF3[data+"_"+subData] = reportData[data][subData]
resultDF3['1_precision'] =round(resultDF3['1_precision']*100,2)
resultDF3['1_recall'] =round(resultDF3['1_recall']*100,2)
resultDF3['1_f1-score'] =round(resultDF3['1_f1-score']*100,2)
resultDF3

  7/189 [>.............................] - ETA: 4s

2022-08-26 12:57:24.500213: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 2s 9ms/step


evaluate_model(model3, X_test, y_test)

189/189 [==============================] - 2s 9ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.70      0.65      0.68      1911
           1       0.85      0.87      0.86      4135

    accuracy                           0.80      6046
   macro avg       0.77      0.76      0.77      6046
weighted avg       0.80      0.80      0.80      6046


-----------------------ROC Curve-----------------------


df =pd.concat([resultDF1,resultDF2,resultDF3],ignore_index=True)
df


## Creating a Copy feature use.
X_train1 = X_train.copy()
X_test1 = X_test.copy()
X_val1 = X_val.copy()


!pwd

/Users/seenu/tensorflow/rsna-pneumonia-detection-challenge


# Insights:

# The best model is Model-1 (with 2 classes) when compared with Model-2 (with 3 classes).

# So, we chosen Model-1 for further analysis.

# The Model-1 with data augmentation performs fairly well with sample images.

# It's tremendously satisfying to see a simple model make substantial headway on learning a difficult task.

# Next steps would include an investigation of alternative model architectures, more robust hyperparameter optimization, 
# and investigating transfer learning using models trained on similar images.


from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.vgg16 import preprocess_input
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from tensorflow.keras import layers, models

base_model = VGG16(weights="imagenet", include_top=False, input_shape=(128,128,3))
base_model.trainable = False ## Not trainable weights


## Adding two hidden later and one softmax layer as an output layer

from tensorflow.keras import layers, models

max_layer = MaxPooling2D(pool_size=(2,2), strides=2)
flatten_layer = layers.Flatten()
dense_layer_1 = layers.Dense(512, activation='relu')
drop_layer_2 = layers.Dropout(0.5)
pred_layer = layers.Dense(1, activation='sigmoid')

CNN_VGG16_model = models.Sequential([
    base_model,
    max_layer,
    flatten_layer,
    dense_layer_1,
    drop_layer_2,
    pred_layer
])


CNN_VGG16_model.summary()

Model: "sequential_3"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 vgg16 (Functional)          (None, 4, 4, 512)         14714688  
                                                                 
 max_pooling2d_12 (MaxPoolin  (None, 2, 2, 512)        0         
 g2D)                                                            
                                                                 
 flatten_3 (Flatten)         (None, 2048)              0         
                                                                 
 dense_6 (Dense)             (None, 512)               1049088   
                                                                 
 dropout_9 (Dropout)         (None, 512)               0         
                                                                 
 dense_7 (Dense)             (None, 1)                 513       
                                                                 
=================================================================
Total params: 15,764,289
Trainable params: 1,049,601
Non-trainable params: 14,714,688
_________________________________________________________________


from tensorflow.keras.callbacks import EarlyStopping

models_loss = 'binary_crossentropy'

models_opt = 'adam'

checkpoint = ModelCheckpoint('Best_CNN_VGG16_model' + '.h5',
                             monitor='val_loss',
                             mode="min",
                             save_best_only=True,
                             verbose=1)

es = EarlyStopping(monitor='val_loss',
                          min_delta=0,
                          patience=10,
                          verbose=1,
                          restore_best_weights=True)

lr_reduction = ReduceLROnPlateau(monitor='val_loss',
                                patience=10,
                                verbose=1,
                                factor=0.8,
                                min_lr=0.0001,
                                mode="auto",
                                min_delta=0.0001,
                                cooldown=5)

callbacks = [checkpoint, es, lr_reduction]

CNN_VGG16_model.compile(loss=models_loss,
              optimizer=models_opt,
              metrics=['accuracy'])


%%time
random_state=22

#Training the model

history4 = CNN_VGG16_model.fit(X_train, 
                              y_train, 
                              epochs=epochs, 
                              validation_data=valid_generator,
                              batch_size = batch_size,
                              callbacks=callbacks)

Epoch 1/30

2022-08-26 13:02:09.231203: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.5026 - accuracy: 0.7636

2022-08-26 13:03:01.275267: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

Epoch 1: val_loss improved from inf to 0.45472, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 70s 380ms/step - loss: 0.5026 - accuracy: 0.7636 - val_loss: 0.4547 - val_accuracy: 0.7825 - lr: 0.0010
Epoch 2/30
182/182 [==============================] - ETA: 0s - loss: 0.4542 - accuracy: 0.7843
Epoch 2: val_loss improved from 0.45472 to 0.44443, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 69s 380ms/step - loss: 0.4542 - accuracy: 0.7843 - val_loss: 0.4444 - val_accuracy: 0.7901 - lr: 0.0010
Epoch 3/30
182/182 [==============================] - ETA: 0s - loss: 0.4432 - accuracy: 0.7882
Epoch 3: val_loss improved from 0.44443 to 0.44210, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 80s 440ms/step - loss: 0.4432 - accuracy: 0.7882 - val_loss: 0.4421 - val_accuracy: 0.7904 - lr: 0.0010
Epoch 4/30
182/182 [==============================] - ETA: 0s - loss: 0.4349 - accuracy: 0.7953
Epoch 4: val_loss improved from 0.44210 to 0.43481, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 69s 379ms/step - loss: 0.4349 - accuracy: 0.7953 - val_loss: 0.4348 - val_accuracy: 0.7937 - lr: 0.0010
Epoch 5/30
182/182 [==============================] - ETA: 0s - loss: 0.4309 - accuracy: 0.7945
Epoch 5: val_loss improved from 0.43481 to 0.42557, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 69s 380ms/step - loss: 0.4309 - accuracy: 0.7945 - val_loss: 0.4256 - val_accuracy: 0.8002 - lr: 0.0010
Epoch 6/30
182/182 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.7962
Epoch 6: val_loss improved from 0.42557 to 0.42472, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 69s 382ms/step - loss: 0.4289 - accuracy: 0.7962 - val_loss: 0.4247 - val_accuracy: 0.8019 - lr: 0.0010
Epoch 7/30
182/182 [==============================] - ETA: 0s - loss: 0.4240 - accuracy: 0.8019
Epoch 7: val_loss improved from 0.42472 to 0.42457, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 68s 376ms/step - loss: 0.4240 - accuracy: 0.8019 - val_loss: 0.4246 - val_accuracy: 0.7995 - lr: 0.0010
Epoch 8/30
182/182 [==============================] - ETA: 0s - loss: 0.4219 - accuracy: 0.7980
Epoch 8: val_loss improved from 0.42457 to 0.42045, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 68s 376ms/step - loss: 0.4219 - accuracy: 0.7980 - val_loss: 0.4205 - val_accuracy: 0.8037 - lr: 0.0010
Epoch 9/30
182/182 [==============================] - ETA: 0s - loss: 0.4157 - accuracy: 0.8031
Epoch 9: val_loss did not improve from 0.42045
182/182 [==============================] - 68s 375ms/step - loss: 0.4157 - accuracy: 0.8031 - val_loss: 0.4240 - val_accuracy: 0.7990 - lr: 0.0010
Epoch 10/30
182/182 [==============================] - ETA: 0s - loss: 0.4134 - accuracy: 0.8070
Epoch 10: val_loss improved from 0.42045 to 0.41996, saving model to Best_CNN_VGG16_model.h5
182/182 [==============================] - 68s 376ms/step - loss: 0.4134 - accuracy: 0.8070 - val_loss: 0.4200 - val_accuracy: 0.7984 - lr: 0.0010
Epoch 11/30
182/182 [==============================] - ETA: 0s - loss: 0.4145 - accuracy: 0.8064
Epoch 11: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 375ms/step - loss: 0.4145 - accuracy: 0.8064 - val_loss: 0.4223 - val_accuracy: 0.8023 - lr: 0.0010
Epoch 12/30
182/182 [==============================] - ETA: 0s - loss: 0.4067 - accuracy: 0.8079
Epoch 12: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 376ms/step - loss: 0.4067 - accuracy: 0.8079 - val_loss: 0.4299 - val_accuracy: 0.7980 - lr: 0.0010
Epoch 13/30
182/182 [==============================] - ETA: 0s - loss: 0.4078 - accuracy: 0.8073
Epoch 13: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 375ms/step - loss: 0.4078 - accuracy: 0.8073 - val_loss: 0.4266 - val_accuracy: 0.7969 - lr: 0.0010
Epoch 14/30
182/182 [==============================] - ETA: 0s - loss: 0.4051 - accuracy: 0.8093
Epoch 14: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 376ms/step - loss: 0.4051 - accuracy: 0.8093 - val_loss: 0.4238 - val_accuracy: 0.7999 - lr: 0.0010
Epoch 15/30
182/182 [==============================] - ETA: 0s - loss: 0.3997 - accuracy: 0.8125
Epoch 15: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 376ms/step - loss: 0.3997 - accuracy: 0.8125 - val_loss: 0.4262 - val_accuracy: 0.7976 - lr: 0.0010
Epoch 16/30
182/182 [==============================] - ETA: 0s - loss: 0.4064 - accuracy: 0.8088
Epoch 16: val_loss did not improve from 0.41996
182/182 [==============================] - 220s 1s/step - loss: 0.4064 - accuracy: 0.8088 - val_loss: 0.4545 - val_accuracy: 0.7808 - lr: 0.0010
Epoch 17/30
182/182 [==============================] - ETA: 0s - loss: 0.3974 - accuracy: 0.8125
Epoch 17: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 377ms/step - loss: 0.3974 - accuracy: 0.8125 - val_loss: 0.4210 - val_accuracy: 0.8019 - lr: 0.0010
Epoch 18/30
182/182 [==============================] - ETA: 0s - loss: 0.3934 - accuracy: 0.8149
Epoch 18: val_loss did not improve from 0.41996
182/182 [==============================] - 68s 376ms/step - loss: 0.3934 - accuracy: 0.8149 - val_loss: 0.4215 - val_accuracy: 0.8023 - lr: 0.0010
Epoch 19/30
182/182 [==============================] - ETA: 0s - loss: 0.3886 - accuracy: 0.8180
Epoch 19: val_loss did not improve from 0.41996
182/182 [==============================] - 208s 1s/step - loss: 0.3886 - accuracy: 0.8180 - val_loss: 0.4218 - val_accuracy: 0.7999 - lr: 0.0010
Epoch 20/30
182/182 [==============================] - ETA: 0s - loss: 0.3834 - accuracy: 0.8197
Epoch 20: val_loss did not improve from 0.41996
Restoring model weights from the end of the best epoch: 10.

Epoch 20: ReduceLROnPlateau reducing learning rate to 0.000800000037997961.
182/182 [==============================] - 69s 377ms/step - loss: 0.3834 - accuracy: 0.8197 - val_loss: 0.4277 - val_accuracy: 0.8023 - lr: 0.0010
Epoch 20: early stopping
CPU times: user 1min 47s, sys: 12min 19s, total: 14min 7s
Wall time: 28min 16s


# Evaluating the acuracy , we have only got accuracy of 74% for both train and test.
model4_test_loss, model4_test_acc = CNN_VGG16_model.evaluate(X_test, y_test, verbose=1)
print('Test Loss of the model is -:', model4_test_loss)
print('Test Accuracy of the model is:', model4_test_acc)

189/189 [==============================] - 18s 92ms/step - loss: 0.4227 - accuracy: 0.7985
Test Loss of the model is -: 0.42267507314682007
Test Accuracy of the model is: 0.7985444664955139


# Plottting the accuracy vs loss graph
train4_acc = history4.history['accuracy']

display_loss_accuracy(history4)


# printing the dataframe
resultDF4 = createResult("CNN_VGG16_model with trainable -False",
                         round(train4_acc[-1]*100),round(model4_test_acc*100,2))
resultDF4


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob4 = CNN_VGG16_model.predict(x=X_test)
y_prob4 = y_prob4.reshape((len(y_prob4),))

    # Convert to binary probabilities
y_hat4   = y_prob4 > threshold
y_hat4   = y_hat4.reshape(len(y_hat4),)

    
reportData = classification_report(y_test, y_hat4,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF4[data+"_"+subData] = reportData[data][subData]
                
resultDF4['1_precision'] =round(resultDF4['1_precision']*100,2)
resultDF4['1_recall'] =round(resultDF4['1_recall']*100,2)
resultDF4['1_f1-score'] =round(resultDF4['1_f1-score']*100,2) 
resultDF4

  2/189 [..............................] - ETA: 10s

2022-08-26 13:36:26.280251: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 17s 89ms/step


evaluate_model(CNN_VGG16_model, X_test, y_test)

189/189 [==============================] - 17s 89ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.73      0.58      0.65      1911
           1       0.82      0.90      0.86      4135

    accuracy                           0.80      6046
   macro avg       0.77      0.74      0.75      6046
weighted avg       0.79      0.80      0.79      6046


-----------------------ROC Curve-----------------------


df =pd.concat([resultDF1,resultDF2,resultDF3,resultDF4],ignore_index=True)
df


base_model1 = VGG16(weights="imagenet", include_top=False, input_shape=(128,128,3))
base_model1.trainable = True ## Not trainable weights


from tensorflow.keras import layers, models

max_layer = MaxPooling2D(pool_size=(2,2), strides=2)
flatten_layer = layers.Flatten()
dense_layer_1 = layers.Dense(512, activation='relu')
drop_layer_2 = layers.Dropout(0.5)
pred_layer = layers.Dense(1, activation='sigmoid')

CNN_VGG16_model1 = models.Sequential([
    base_model1,
    max_layer,
    flatten_layer,
    dense_layer_1,
    drop_layer_2,
    pred_layer
])


CNN_VGG16_model1.compile(loss=models_loss,
              optimizer=models_opt,
              metrics=['accuracy'])


%%time
random_state=22

#Training the model

history5 = CNN_VGG16_model1.fit(X_train, 
                              y_train, 
                              epochs=epochs, 
                              validation_data=valid_generator,
                              batch_size = batch_size,
                              callbacks=callbacks)

Epoch 1/30

2022-08-26 13:37:37.004316: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.6649 - accuracy: 0.6770

2022-08-26 13:40:13.466632: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

Epoch 1: val_loss did not improve from 0.41996
182/182 [==============================] - 172s 937ms/step - loss: 0.6649 - accuracy: 0.6770 - val_loss: 0.6249 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 2/30
182/182 [==============================] - ETA: 0s - loss: 0.6252 - accuracy: 0.6839
Epoch 2: val_loss did not improve from 0.41996
182/182 [==============================] - 181s 992ms/step - loss: 0.6252 - accuracy: 0.6839 - val_loss: 0.6239 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 3/30
182/182 [==============================] - ETA: 0s - loss: 0.6240 - accuracy: 0.6839
Epoch 3: val_loss did not improve from 0.41996
182/182 [==============================] - 171s 938ms/step - loss: 0.6240 - accuracy: 0.6839 - val_loss: 0.6239 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 4/30
182/182 [==============================] - ETA: 0s - loss: 0.6247 - accuracy: 0.6839
Epoch 4: val_loss did not improve from 0.41996
182/182 [==============================] - 170s 935ms/step - loss: 0.6247 - accuracy: 0.6839 - val_loss: 0.6241 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 5/30
182/182 [==============================] - ETA: 0s - loss: 0.6249 - accuracy: 0.6839
Epoch 5: val_loss did not improve from 0.41996
182/182 [==============================] - 171s 938ms/step - loss: 0.6249 - accuracy: 0.6839 - val_loss: 0.6239 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 6/30
182/182 [==============================] - ETA: 0s - loss: 0.6243 - accuracy: 0.6839
Epoch 6: val_loss did not improve from 0.41996
182/182 [==============================] - 170s 936ms/step - loss: 0.6243 - accuracy: 0.6839 - val_loss: 0.6240 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 7/30
182/182 [==============================] - ETA: 0s - loss: 0.6251 - accuracy: 0.6839
Epoch 7: val_loss did not improve from 0.41996
182/182 [==============================] - 170s 936ms/step - loss: 0.6251 - accuracy: 0.6839 - val_loss: 0.6240 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 8/30
182/182 [==============================] - ETA: 0s - loss: 0.6245 - accuracy: 0.6839
Epoch 8: val_loss did not improve from 0.41996
182/182 [==============================] - 170s 936ms/step - loss: 0.6245 - accuracy: 0.6839 - val_loss: 0.6242 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 9/30
182/182 [==============================] - ETA: 0s - loss: 0.6245 - accuracy: 0.6839
Epoch 9: val_loss did not improve from 0.41996
182/182 [==============================] - 171s 938ms/step - loss: 0.6245 - accuracy: 0.6839 - val_loss: 0.6239 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 10/30
182/182 [==============================] - ETA: 0s - loss: 0.6242 - accuracy: 0.6839
Epoch 10: val_loss did not improve from 0.41996
182/182 [==============================] - 171s 939ms/step - loss: 0.6242 - accuracy: 0.6839 - val_loss: 0.6243 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 11/30
182/182 [==============================] - ETA: 0s - loss: 0.6249 - accuracy: 0.6839
Epoch 11: val_loss did not improve from 0.41996
182/182 [==============================] - 170s 937ms/step - loss: 0.6249 - accuracy: 0.6839 - val_loss: 0.6239 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 12/30
182/182 [==============================] - ETA: 0s - loss: 0.6246 - accuracy: 0.6839
Epoch 12: val_loss did not improve from 0.41996
Restoring model weights from the end of the best epoch: 2.

Epoch 12: ReduceLROnPlateau reducing learning rate to 0.000800000037997961.
182/182 [==============================] - 171s 937ms/step - loss: 0.6246 - accuracy: 0.6839 - val_loss: 0.6246 - val_accuracy: 0.6839 - lr: 0.0010
Epoch 12: early stopping
CPU times: user 1min 16s, sys: 10min 26s, total: 11min 42s
Wall time: 34min 36s


#CNN_VGG16_model1.save('best_VGG16_model_trainable.h5')


#best_model = tf.keras.models.load_model('best_VGG16_model_trainable.h5')


model5_test_loss, model5_test_acc = CNN_VGG16_model1.evaluate(X_test, y_test, verbose=1)
print('Test Loss of the model is -:', model5_test_loss)
print('Test Accuracy of the model is:', model5_test_acc)

189/189 [==============================] - 15s 79ms/step - loss: 0.6239 - accuracy: 0.6839
Test Loss of the model is -: 0.6238886713981628
Test Accuracy of the model is: 0.6839232444763184


#Plottting the accuracy vs loss graph
train5_acc = history5.history['accuracy']
#Plottting the accuracy vs loss graph
display_loss_accuracy(history5)


# printing the dataframe
resultDF5 = createResult("CNN_VGG16_model with trainable -True",
                         round(train5_acc[-1]*100,2),round(model5_test_acc*100,2))
resultDF5


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob5 = CNN_VGG16_model1.predict(x=X_test)
y_prob5 = y_prob5.reshape((len(y_prob5),))

    # Convert to binary probabilities
y_hat5   = y_prob5 > threshold
y_hat5   = y_hat5.reshape(len(y_hat5),)

    
reportData = classification_report(y_test, y_hat5,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF5[data+"_"+subData] = reportData[data][subData]

resultDF5['1_precision'] =round(resultDF5['1_precision']*100,2)
resultDF5['1_recall'] =round(resultDF5['1_recall']*100,2)
resultDF5['1_f1-score'] =round(resultDF5['1_f1-score']*100,2)
resultDF5

  1/189 [..............................] - ETA: 56s

2022-08-26 14:21:10.430084: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 17s 90ms/step


evaluate_model(CNN_VGG16_model1, X_test, y_test)

189/189 [==============================] - 17s 89ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.00      0.00      0.00      1911
           1       0.68      1.00      0.81      4135

    accuracy                           0.68      6046
   macro avg       0.34      0.50      0.41      6046
weighted avg       0.47      0.68      0.56      6046


-----------------------ROC Curve-----------------------


df =pd.concat([resultDF1,resultDF2,resultDF3,resultDF4,resultDF5],ignore_index=True)
df


from keras.applications import InceptionV3

inception_base_model = InceptionV3(input_shape=(128,128,3),include_top=False,weights='imagenet')


inception_model = Sequential([
        inception_base_model,
        GlobalAveragePooling2D(),
        Dense(512, activation="relu"),
        BatchNormalization(),
        Dropout(0.6),
        Dense(128, activation="relu"),
        BatchNormalization(),
        Dropout(0.4),
        Dense(64,activation="relu"),
        BatchNormalization(),
        Dropout(0.3),
        Dense(1,activation="sigmoid")
    ])


%%time
random_state=22
#opt = tf.keras.optimizers.Adam(lr=0.001)
    
inception_model.compile(optimizer = RMSprop(learning_rate=0.0001), 
                        loss = 'binary_crossentropy',metrics=['accuracy'])

history6 = inception_model.fit(train_generator,
                    batch_size = batch_size,
                    epochs = epochs,
                    validation_data = valid_generator,
                    verbose = 1)

Epoch 1/30

2022-08-26 14:32:50.602792: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.7606 - accuracy: 0.6233

2022-08-26 14:34:18.927969: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - 100s 470ms/step - loss: 0.7606 - accuracy: 0.6233 - val_loss: 0.8997 - val_accuracy: 0.6530
Epoch 2/30
182/182 [==============================] - 688s 4s/step - loss: 0.5992 - accuracy: 0.7251 - val_loss: 0.6641 - val_accuracy: 0.7244
Epoch 3/30
182/182 [==============================] - 79s 432ms/step - loss: 0.5146 - accuracy: 0.7695 - val_loss: 0.5743 - val_accuracy: 0.7890
Epoch 4/30
182/182 [==============================] - 154s 848ms/step - loss: 0.4736 - accuracy: 0.7882 - val_loss: 0.4506 - val_accuracy: 0.8167
Epoch 5/30
182/182 [==============================] - 79s 430ms/step - loss: 0.4411 - accuracy: 0.8015 - val_loss: 0.4425 - val_accuracy: 0.8126
Epoch 6/30
182/182 [==============================] - 421s 2s/step - loss: 0.4246 - accuracy: 0.8087 - val_loss: 0.4848 - val_accuracy: 0.8146
Epoch 7/30
182/182 [==============================] - 79s 430ms/step - loss: 0.4136 - accuracy: 0.8124 - val_loss: 0.4889 - val_accuracy: 0.8273
Epoch 8/30
182/182 [==============================] - 79s 431ms/step - loss: 0.4054 - accuracy: 0.8199 - val_loss: 0.4801 - val_accuracy: 0.8015
Epoch 9/30
182/182 [==============================] - 79s 432ms/step - loss: 0.3977 - accuracy: 0.8210 - val_loss: 0.4394 - val_accuracy: 0.8156
Epoch 10/30
182/182 [==============================] - 87s 474ms/step - loss: 0.3904 - accuracy: 0.8227 - val_loss: 0.4784 - val_accuracy: 0.8119
Epoch 11/30
182/182 [==============================] - 89s 484ms/step - loss: 0.3819 - accuracy: 0.8268 - val_loss: 0.4036 - val_accuracy: 0.8301
Epoch 12/30
182/182 [==============================] - 80s 440ms/step - loss: 0.3727 - accuracy: 0.8281 - val_loss: 0.5054 - val_accuracy: 0.8232
Epoch 13/30
182/182 [==============================] - 79s 432ms/step - loss: 0.3696 - accuracy: 0.8310 - val_loss: 0.4827 - val_accuracy: 0.8037
Epoch 14/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3603 - accuracy: 0.8374 - val_loss: 0.4979 - val_accuracy: 0.7911
Epoch 15/30
182/182 [==============================] - 79s 434ms/step - loss: 0.3555 - accuracy: 0.8413 - val_loss: 0.4392 - val_accuracy: 0.8277
Epoch 16/30
182/182 [==============================] - 79s 433ms/step - loss: 0.3515 - accuracy: 0.8405 - val_loss: 0.4269 - val_accuracy: 0.8348
Epoch 17/30
182/182 [==============================] - 79s 432ms/step - loss: 0.3474 - accuracy: 0.8412 - val_loss: 0.4276 - val_accuracy: 0.8277
Epoch 18/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3380 - accuracy: 0.8474 - val_loss: 0.4392 - val_accuracy: 0.8313
Epoch 19/30
182/182 [==============================] - 79s 429ms/step - loss: 0.3347 - accuracy: 0.8500 - val_loss: 0.4298 - val_accuracy: 0.8227
Epoch 20/30
182/182 [==============================] - 79s 430ms/step - loss: 0.3216 - accuracy: 0.8598 - val_loss: 0.3985 - val_accuracy: 0.8250
Epoch 21/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3168 - accuracy: 0.8581 - val_loss: 0.4265 - val_accuracy: 0.8311
Epoch 22/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3128 - accuracy: 0.8633 - val_loss: 0.4714 - val_accuracy: 0.8296
Epoch 23/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3107 - accuracy: 0.8647 - val_loss: 0.4494 - val_accuracy: 0.8290
Epoch 24/30
182/182 [==============================] - 79s 431ms/step - loss: 0.3025 - accuracy: 0.8677 - val_loss: 0.4395 - val_accuracy: 0.8295
Epoch 25/30
182/182 [==============================] - 79s 431ms/step - loss: 0.2929 - accuracy: 0.8720 - val_loss: 0.4836 - val_accuracy: 0.8161
Epoch 26/30
182/182 [==============================] - 79s 431ms/step - loss: 0.2843 - accuracy: 0.8780 - val_loss: 0.5295 - val_accuracy: 0.8247
Epoch 27/30
182/182 [==============================] - 79s 434ms/step - loss: 0.2773 - accuracy: 0.8805 - val_loss: 0.5318 - val_accuracy: 0.8205
Epoch 28/30
182/182 [==============================] - 79s 430ms/step - loss: 0.2721 - accuracy: 0.8850 - val_loss: 0.4881 - val_accuracy: 0.8353
Epoch 29/30
182/182 [==============================] - 79s 431ms/step - loss: 0.2652 - accuracy: 0.8888 - val_loss: 0.4432 - val_accuracy: 0.8282
Epoch 30/30
182/182 [==============================] - 79s 431ms/step - loss: 0.2631 - accuracy: 0.8892 - val_loss: 0.5472 - val_accuracy: 0.8215
CPU times: user 28min 49s, sys: 9min 39s, total: 38min 28s
Wall time: 57min 17s


model6_test_loss, model6_test_acc = inception_model.evaluate(X_test, y_test, verbose=1)
print('Test Loss of the model is -:', model6_test_loss)
print('Test Accuracy of the model is:', model6_test_acc)

189/189 [==============================] - 9s 43ms/step - loss: 0.5557 - accuracy: 0.8204
Test Loss of the model is -: 0.5556652545928955
Test Accuracy of the model is: 0.8203771114349365


train6_acc = history6.history['accuracy']
#Plottting the accuracy vs loss graph
display_loss_accuracy(history6)


# printing the dataframe
resultDF6 = createResult("inception_base_model",
                         round(train6_acc[-1]*100,2),round(model6_test_acc*100,2))
resultDF6


evaluate_model(inception_model, X_test, y_test)

2022-08-26 15:30:19.867196: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 10s 44ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.83      0.54      0.66      1911
           1       0.82      0.95      0.88      4135

    accuracy                           0.82      6046
   macro avg       0.83      0.74      0.77      6046
weighted avg       0.82      0.82      0.81      6046


-----------------------ROC Curve-----------------------


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob6 = inception_model.predict(x=X_test)
y_prob6 = y_prob6.reshape((len(y_prob6),))

    # Convert to binary probabilities
y_hat6   = y_prob6 > threshold
y_hat6   = y_hat6.reshape(len(y_hat6),)

    
reportData = classification_report(y_test, y_hat6,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF6[data+"_"+subData] = reportData[data][subData]
resultDF6['1_precision'] =round(resultDF6['1_precision']*100,2)
resultDF6['1_recall'] =round(resultDF6['1_recall']*100,2)
resultDF6['1_f1-score'] =round(resultDF6['1_f1-score']*100,2)
resultDF6

189/189 [==============================] - 7s 36ms/step


df =pd.concat([resultDF1,resultDF2,resultDF3,resultDF4,
              resultDF5,resultDF6],ignore_index=True)
df


from keras.applications.densenet import DenseNet121
from keras.layers import Dense, GlobalAveragePooling2D
from keras.models import Model
from keras import backend as K

base_model = DenseNet121(input_shape=(128, 128, 3), include_top=False, weights='imagenet', pooling='avg')

base_model.summary()

Model: "densenet121"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_4 (InputLayer)           [(None, 128, 128, 3  0           []                               
                                )]                                                                
                                                                                                  
 zero_padding2d (ZeroPadding2D)  (None, 134, 134, 3)  0          ['input_4[0][0]']                
                                                                                                  
 conv1/conv (Conv2D)            (None, 64, 64, 64)   9408        ['zero_padding2d[0][0]']         
                                                                                                  
 conv1/bn (BatchNormalization)  (None, 64, 64, 64)   256         ['conv1/conv[0][0]']             
                                                                                                  
 conv1/relu (Activation)        (None, 64, 64, 64)   0           ['conv1/bn[0][0]']               
                                                                                                  
 zero_padding2d_1 (ZeroPadding2  (None, 66, 66, 64)  0           ['conv1/relu[0][0]']             
 D)                                                                                               
                                                                                                  
 pool1 (MaxPooling2D)           (None, 32, 32, 64)   0           ['zero_padding2d_1[0][0]']       
                                                                                                  
 conv2_block1_0_bn (BatchNormal  (None, 32, 32, 64)  256         ['pool1[0][0]']                  
 ization)                                                                                         
                                                                                                  
 conv2_block1_0_relu (Activatio  (None, 32, 32, 64)  0           ['conv2_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_1_conv (Conv2D)   (None, 32, 32, 128)  8192        ['conv2_block1_0_relu[0][0]']    
                                                                                                  
 conv2_block1_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block1_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block1_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block1_1_relu[0][0]']    
                                                                                                  
 conv2_block1_concat (Concatena  (None, 32, 32, 96)  0           ['pool1[0][0]',                  
 te)                                                              'conv2_block1_2_conv[0][0]']    
                                                                                                  
 conv2_block2_0_bn (BatchNormal  (None, 32, 32, 96)  384         ['conv2_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_0_relu (Activatio  (None, 32, 32, 96)  0           ['conv2_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_1_conv (Conv2D)   (None, 32, 32, 128)  12288       ['conv2_block2_0_relu[0][0]']    
                                                                                                  
 conv2_block2_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block2_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block2_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block2_1_relu[0][0]']    
                                                                                                  
 conv2_block2_concat (Concatena  (None, 32, 32, 128)  0          ['conv2_block1_concat[0][0]',    
 te)                                                              'conv2_block2_2_conv[0][0]']    
                                                                                                  
 conv2_block3_0_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_0_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_1_conv (Conv2D)   (None, 32, 32, 128)  16384       ['conv2_block3_0_relu[0][0]']    
                                                                                                  
 conv2_block3_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block3_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block3_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block3_1_relu[0][0]']    
                                                                                                  
 conv2_block3_concat (Concatena  (None, 32, 32, 160)  0          ['conv2_block2_concat[0][0]',    
 te)                                                              'conv2_block3_2_conv[0][0]']    
                                                                                                  
 conv2_block4_0_bn (BatchNormal  (None, 32, 32, 160)  640        ['conv2_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_0_relu (Activatio  (None, 32, 32, 160)  0          ['conv2_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_1_conv (Conv2D)   (None, 32, 32, 128)  20480       ['conv2_block4_0_relu[0][0]']    
                                                                                                  
 conv2_block4_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block4_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block4_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block4_1_relu[0][0]']    
                                                                                                  
 conv2_block4_concat (Concatena  (None, 32, 32, 192)  0          ['conv2_block3_concat[0][0]',    
 te)                                                              'conv2_block4_2_conv[0][0]']    
                                                                                                  
 conv2_block5_0_bn (BatchNormal  (None, 32, 32, 192)  768        ['conv2_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_0_relu (Activatio  (None, 32, 32, 192)  0          ['conv2_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_1_conv (Conv2D)   (None, 32, 32, 128)  24576       ['conv2_block5_0_relu[0][0]']    
                                                                                                  
 conv2_block5_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block5_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block5_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block5_1_relu[0][0]']    
                                                                                                  
 conv2_block5_concat (Concatena  (None, 32, 32, 224)  0          ['conv2_block4_concat[0][0]',    
 te)                                                              'conv2_block5_2_conv[0][0]']    
                                                                                                  
 conv2_block6_0_bn (BatchNormal  (None, 32, 32, 224)  896        ['conv2_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_0_relu (Activatio  (None, 32, 32, 224)  0          ['conv2_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_1_conv (Conv2D)   (None, 32, 32, 128)  28672       ['conv2_block6_0_relu[0][0]']    
                                                                                                  
 conv2_block6_1_bn (BatchNormal  (None, 32, 32, 128)  512        ['conv2_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv2_block6_1_relu (Activatio  (None, 32, 32, 128)  0          ['conv2_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv2_block6_2_conv (Conv2D)   (None, 32, 32, 32)   36864       ['conv2_block6_1_relu[0][0]']    
                                                                                                  
 conv2_block6_concat (Concatena  (None, 32, 32, 256)  0          ['conv2_block5_concat[0][0]',    
 te)                                                              'conv2_block6_2_conv[0][0]']    
                                                                                                  
 pool2_bn (BatchNormalization)  (None, 32, 32, 256)  1024        ['conv2_block6_concat[0][0]']    
                                                                                                  
 pool2_relu (Activation)        (None, 32, 32, 256)  0           ['pool2_bn[0][0]']               
                                                                                                  
 pool2_conv (Conv2D)            (None, 32, 32, 128)  32768       ['pool2_relu[0][0]']             
                                                                                                  
 pool2_pool (AveragePooling2D)  (None, 16, 16, 128)  0           ['pool2_conv[0][0]']             
                                                                                                  
 conv3_block1_0_bn (BatchNormal  (None, 16, 16, 128)  512        ['pool2_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv3_block1_0_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_1_conv (Conv2D)   (None, 16, 16, 128)  16384       ['conv3_block1_0_relu[0][0]']    
                                                                                                  
 conv3_block1_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block1_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block1_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block1_1_relu[0][0]']    
                                                                                                  
 conv3_block1_concat (Concatena  (None, 16, 16, 160)  0          ['pool2_pool[0][0]',             
 te)                                                              'conv3_block1_2_conv[0][0]']    
                                                                                                  
 conv3_block2_0_bn (BatchNormal  (None, 16, 16, 160)  640        ['conv3_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_0_relu (Activatio  (None, 16, 16, 160)  0          ['conv3_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_1_conv (Conv2D)   (None, 16, 16, 128)  20480       ['conv3_block2_0_relu[0][0]']    
                                                                                                  
 conv3_block2_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block2_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block2_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block2_1_relu[0][0]']    
                                                                                                  
 conv3_block2_concat (Concatena  (None, 16, 16, 192)  0          ['conv3_block1_concat[0][0]',    
 te)                                                              'conv3_block2_2_conv[0][0]']    
                                                                                                  
 conv3_block3_0_bn (BatchNormal  (None, 16, 16, 192)  768        ['conv3_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_0_relu (Activatio  (None, 16, 16, 192)  0          ['conv3_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_1_conv (Conv2D)   (None, 16, 16, 128)  24576       ['conv3_block3_0_relu[0][0]']    
                                                                                                  
 conv3_block3_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block3_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block3_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block3_1_relu[0][0]']    
                                                                                                  
 conv3_block3_concat (Concatena  (None, 16, 16, 224)  0          ['conv3_block2_concat[0][0]',    
 te)                                                              'conv3_block3_2_conv[0][0]']    
                                                                                                  
 conv3_block4_0_bn (BatchNormal  (None, 16, 16, 224)  896        ['conv3_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_0_relu (Activatio  (None, 16, 16, 224)  0          ['conv3_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_1_conv (Conv2D)   (None, 16, 16, 128)  28672       ['conv3_block4_0_relu[0][0]']    
                                                                                                  
 conv3_block4_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block4_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block4_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block4_1_relu[0][0]']    
                                                                                                  
 conv3_block4_concat (Concatena  (None, 16, 16, 256)  0          ['conv3_block3_concat[0][0]',    
 te)                                                              'conv3_block4_2_conv[0][0]']    
                                                                                                  
 conv3_block5_0_bn (BatchNormal  (None, 16, 16, 256)  1024       ['conv3_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_0_relu (Activatio  (None, 16, 16, 256)  0          ['conv3_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_1_conv (Conv2D)   (None, 16, 16, 128)  32768       ['conv3_block5_0_relu[0][0]']    
                                                                                                  
 conv3_block5_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block5_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block5_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block5_1_relu[0][0]']    
                                                                                                  
 conv3_block5_concat (Concatena  (None, 16, 16, 288)  0          ['conv3_block4_concat[0][0]',    
 te)                                                              'conv3_block5_2_conv[0][0]']    
                                                                                                  
 conv3_block6_0_bn (BatchNormal  (None, 16, 16, 288)  1152       ['conv3_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_0_relu (Activatio  (None, 16, 16, 288)  0          ['conv3_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_1_conv (Conv2D)   (None, 16, 16, 128)  36864       ['conv3_block6_0_relu[0][0]']    
                                                                                                  
 conv3_block6_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block6_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block6_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block6_1_relu[0][0]']    
                                                                                                  
 conv3_block6_concat (Concatena  (None, 16, 16, 320)  0          ['conv3_block5_concat[0][0]',    
 te)                                                              'conv3_block6_2_conv[0][0]']    
                                                                                                  
 conv3_block7_0_bn (BatchNormal  (None, 16, 16, 320)  1280       ['conv3_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_0_relu (Activatio  (None, 16, 16, 320)  0          ['conv3_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_1_conv (Conv2D)   (None, 16, 16, 128)  40960       ['conv3_block7_0_relu[0][0]']    
                                                                                                  
 conv3_block7_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block7_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block7_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block7_1_relu[0][0]']    
                                                                                                  
 conv3_block7_concat (Concatena  (None, 16, 16, 352)  0          ['conv3_block6_concat[0][0]',    
 te)                                                              'conv3_block7_2_conv[0][0]']    
                                                                                                  
 conv3_block8_0_bn (BatchNormal  (None, 16, 16, 352)  1408       ['conv3_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_0_relu (Activatio  (None, 16, 16, 352)  0          ['conv3_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_1_conv (Conv2D)   (None, 16, 16, 128)  45056       ['conv3_block8_0_relu[0][0]']    
                                                                                                  
 conv3_block8_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block8_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block8_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block8_1_relu[0][0]']    
                                                                                                  
 conv3_block8_concat (Concatena  (None, 16, 16, 384)  0          ['conv3_block7_concat[0][0]',    
 te)                                                              'conv3_block8_2_conv[0][0]']    
                                                                                                  
 conv3_block9_0_bn (BatchNormal  (None, 16, 16, 384)  1536       ['conv3_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_0_relu (Activatio  (None, 16, 16, 384)  0          ['conv3_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_1_conv (Conv2D)   (None, 16, 16, 128)  49152       ['conv3_block9_0_relu[0][0]']    
                                                                                                  
 conv3_block9_1_bn (BatchNormal  (None, 16, 16, 128)  512        ['conv3_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv3_block9_1_relu (Activatio  (None, 16, 16, 128)  0          ['conv3_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv3_block9_2_conv (Conv2D)   (None, 16, 16, 32)   36864       ['conv3_block9_1_relu[0][0]']    
                                                                                                  
 conv3_block9_concat (Concatena  (None, 16, 16, 416)  0          ['conv3_block8_concat[0][0]',    
 te)                                                              'conv3_block9_2_conv[0][0]']    
                                                                                                  
 conv3_block10_0_bn (BatchNorma  (None, 16, 16, 416)  1664       ['conv3_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv3_block10_0_relu (Activati  (None, 16, 16, 416)  0          ['conv3_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_1_conv (Conv2D)  (None, 16, 16, 128)  53248       ['conv3_block10_0_relu[0][0]']   
                                                                                                  
 conv3_block10_1_bn (BatchNorma  (None, 16, 16, 128)  512        ['conv3_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block10_1_relu (Activati  (None, 16, 16, 128)  0          ['conv3_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block10_2_conv (Conv2D)  (None, 16, 16, 32)   36864       ['conv3_block10_1_relu[0][0]']   
                                                                                                  
 conv3_block10_concat (Concaten  (None, 16, 16, 448)  0          ['conv3_block9_concat[0][0]',    
 ate)                                                             'conv3_block10_2_conv[0][0]']   
                                                                                                  
 conv3_block11_0_bn (BatchNorma  (None, 16, 16, 448)  1792       ['conv3_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_0_relu (Activati  (None, 16, 16, 448)  0          ['conv3_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_1_conv (Conv2D)  (None, 16, 16, 128)  57344       ['conv3_block11_0_relu[0][0]']   
                                                                                                  
 conv3_block11_1_bn (BatchNorma  (None, 16, 16, 128)  512        ['conv3_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block11_1_relu (Activati  (None, 16, 16, 128)  0          ['conv3_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block11_2_conv (Conv2D)  (None, 16, 16, 32)   36864       ['conv3_block11_1_relu[0][0]']   
                                                                                                  
 conv3_block11_concat (Concaten  (None, 16, 16, 480)  0          ['conv3_block10_concat[0][0]',   
 ate)                                                             'conv3_block11_2_conv[0][0]']   
                                                                                                  
 conv3_block12_0_bn (BatchNorma  (None, 16, 16, 480)  1920       ['conv3_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_0_relu (Activati  (None, 16, 16, 480)  0          ['conv3_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_1_conv (Conv2D)  (None, 16, 16, 128)  61440       ['conv3_block12_0_relu[0][0]']   
                                                                                                  
 conv3_block12_1_bn (BatchNorma  (None, 16, 16, 128)  512        ['conv3_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv3_block12_1_relu (Activati  (None, 16, 16, 128)  0          ['conv3_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv3_block12_2_conv (Conv2D)  (None, 16, 16, 32)   36864       ['conv3_block12_1_relu[0][0]']   
                                                                                                  
 conv3_block12_concat (Concaten  (None, 16, 16, 512)  0          ['conv3_block11_concat[0][0]',   
 ate)                                                             'conv3_block12_2_conv[0][0]']   
                                                                                                  
 pool3_bn (BatchNormalization)  (None, 16, 16, 512)  2048        ['conv3_block12_concat[0][0]']   
                                                                                                  
 pool3_relu (Activation)        (None, 16, 16, 512)  0           ['pool3_bn[0][0]']               
                                                                                                  
 pool3_conv (Conv2D)            (None, 16, 16, 256)  131072      ['pool3_relu[0][0]']             
                                                                                                  
 pool3_pool (AveragePooling2D)  (None, 8, 8, 256)    0           ['pool3_conv[0][0]']             
                                                                                                  
 conv4_block1_0_bn (BatchNormal  (None, 8, 8, 256)   1024        ['pool3_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv4_block1_0_relu (Activatio  (None, 8, 8, 256)   0           ['conv4_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_1_conv (Conv2D)   (None, 8, 8, 128)    32768       ['conv4_block1_0_relu[0][0]']    
                                                                                                  
 conv4_block1_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block1_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block1_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block1_1_relu[0][0]']    
                                                                                                  
 conv4_block1_concat (Concatena  (None, 8, 8, 288)   0           ['pool3_pool[0][0]',             
 te)                                                              'conv4_block1_2_conv[0][0]']    
                                                                                                  
 conv4_block2_0_bn (BatchNormal  (None, 8, 8, 288)   1152        ['conv4_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_0_relu (Activatio  (None, 8, 8, 288)   0           ['conv4_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_1_conv (Conv2D)   (None, 8, 8, 128)    36864       ['conv4_block2_0_relu[0][0]']    
                                                                                                  
 conv4_block2_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block2_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block2_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block2_1_relu[0][0]']    
                                                                                                  
 conv4_block2_concat (Concatena  (None, 8, 8, 320)   0           ['conv4_block1_concat[0][0]',    
 te)                                                              'conv4_block2_2_conv[0][0]']    
                                                                                                  
 conv4_block3_0_bn (BatchNormal  (None, 8, 8, 320)   1280        ['conv4_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_0_relu (Activatio  (None, 8, 8, 320)   0           ['conv4_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_1_conv (Conv2D)   (None, 8, 8, 128)    40960       ['conv4_block3_0_relu[0][0]']    
                                                                                                  
 conv4_block3_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block3_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block3_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block3_1_relu[0][0]']    
                                                                                                  
 conv4_block3_concat (Concatena  (None, 8, 8, 352)   0           ['conv4_block2_concat[0][0]',    
 te)                                                              'conv4_block3_2_conv[0][0]']    
                                                                                                  
 conv4_block4_0_bn (BatchNormal  (None, 8, 8, 352)   1408        ['conv4_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_0_relu (Activatio  (None, 8, 8, 352)   0           ['conv4_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_1_conv (Conv2D)   (None, 8, 8, 128)    45056       ['conv4_block4_0_relu[0][0]']    
                                                                                                  
 conv4_block4_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block4_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block4_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block4_1_relu[0][0]']    
                                                                                                  
 conv4_block4_concat (Concatena  (None, 8, 8, 384)   0           ['conv4_block3_concat[0][0]',    
 te)                                                              'conv4_block4_2_conv[0][0]']    
                                                                                                  
 conv4_block5_0_bn (BatchNormal  (None, 8, 8, 384)   1536        ['conv4_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_0_relu (Activatio  (None, 8, 8, 384)   0           ['conv4_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_1_conv (Conv2D)   (None, 8, 8, 128)    49152       ['conv4_block5_0_relu[0][0]']    
                                                                                                  
 conv4_block5_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block5_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block5_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block5_1_relu[0][0]']    
                                                                                                  
 conv4_block5_concat (Concatena  (None, 8, 8, 416)   0           ['conv4_block4_concat[0][0]',    
 te)                                                              'conv4_block5_2_conv[0][0]']    
                                                                                                  
 conv4_block6_0_bn (BatchNormal  (None, 8, 8, 416)   1664        ['conv4_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_0_relu (Activatio  (None, 8, 8, 416)   0           ['conv4_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_1_conv (Conv2D)   (None, 8, 8, 128)    53248       ['conv4_block6_0_relu[0][0]']    
                                                                                                  
 conv4_block6_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block6_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block6_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block6_1_relu[0][0]']    
                                                                                                  
 conv4_block6_concat (Concatena  (None, 8, 8, 448)   0           ['conv4_block5_concat[0][0]',    
 te)                                                              'conv4_block6_2_conv[0][0]']    
                                                                                                  
 conv4_block7_0_bn (BatchNormal  (None, 8, 8, 448)   1792        ['conv4_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_0_relu (Activatio  (None, 8, 8, 448)   0           ['conv4_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_1_conv (Conv2D)   (None, 8, 8, 128)    57344       ['conv4_block7_0_relu[0][0]']    
                                                                                                  
 conv4_block7_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block7_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block7_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block7_1_relu[0][0]']    
                                                                                                  
 conv4_block7_concat (Concatena  (None, 8, 8, 480)   0           ['conv4_block6_concat[0][0]',    
 te)                                                              'conv4_block7_2_conv[0][0]']    
                                                                                                  
 conv4_block8_0_bn (BatchNormal  (None, 8, 8, 480)   1920        ['conv4_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_0_relu (Activatio  (None, 8, 8, 480)   0           ['conv4_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_1_conv (Conv2D)   (None, 8, 8, 128)    61440       ['conv4_block8_0_relu[0][0]']    
                                                                                                  
 conv4_block8_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block8_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block8_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block8_1_relu[0][0]']    
                                                                                                  
 conv4_block8_concat (Concatena  (None, 8, 8, 512)   0           ['conv4_block7_concat[0][0]',    
 te)                                                              'conv4_block8_2_conv[0][0]']    
                                                                                                  
 conv4_block9_0_bn (BatchNormal  (None, 8, 8, 512)   2048        ['conv4_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_0_relu (Activatio  (None, 8, 8, 512)   0           ['conv4_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_1_conv (Conv2D)   (None, 8, 8, 128)    65536       ['conv4_block9_0_relu[0][0]']    
                                                                                                  
 conv4_block9_1_bn (BatchNormal  (None, 8, 8, 128)   512         ['conv4_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv4_block9_1_relu (Activatio  (None, 8, 8, 128)   0           ['conv4_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv4_block9_2_conv (Conv2D)   (None, 8, 8, 32)     36864       ['conv4_block9_1_relu[0][0]']    
                                                                                                  
 conv4_block9_concat (Concatena  (None, 8, 8, 544)   0           ['conv4_block8_concat[0][0]',    
 te)                                                              'conv4_block9_2_conv[0][0]']    
                                                                                                  
 conv4_block10_0_bn (BatchNorma  (None, 8, 8, 544)   2176        ['conv4_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv4_block10_0_relu (Activati  (None, 8, 8, 544)   0           ['conv4_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_1_conv (Conv2D)  (None, 8, 8, 128)    69632       ['conv4_block10_0_relu[0][0]']   
                                                                                                  
 conv4_block10_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block10_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block10_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block10_1_relu[0][0]']   
                                                                                                  
 conv4_block10_concat (Concaten  (None, 8, 8, 576)   0           ['conv4_block9_concat[0][0]',    
 ate)                                                             'conv4_block10_2_conv[0][0]']   
                                                                                                  
 conv4_block11_0_bn (BatchNorma  (None, 8, 8, 576)   2304        ['conv4_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_0_relu (Activati  (None, 8, 8, 576)   0           ['conv4_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_1_conv (Conv2D)  (None, 8, 8, 128)    73728       ['conv4_block11_0_relu[0][0]']   
                                                                                                  
 conv4_block11_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block11_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block11_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block11_1_relu[0][0]']   
                                                                                                  
 conv4_block11_concat (Concaten  (None, 8, 8, 608)   0           ['conv4_block10_concat[0][0]',   
 ate)                                                             'conv4_block11_2_conv[0][0]']   
                                                                                                  
 conv4_block12_0_bn (BatchNorma  (None, 8, 8, 608)   2432        ['conv4_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_0_relu (Activati  (None, 8, 8, 608)   0           ['conv4_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_1_conv (Conv2D)  (None, 8, 8, 128)    77824       ['conv4_block12_0_relu[0][0]']   
                                                                                                  
 conv4_block12_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block12_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block12_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block12_1_relu[0][0]']   
                                                                                                  
 conv4_block12_concat (Concaten  (None, 8, 8, 640)   0           ['conv4_block11_concat[0][0]',   
 ate)                                                             'conv4_block12_2_conv[0][0]']   
                                                                                                  
 conv4_block13_0_bn (BatchNorma  (None, 8, 8, 640)   2560        ['conv4_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_0_relu (Activati  (None, 8, 8, 640)   0           ['conv4_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_1_conv (Conv2D)  (None, 8, 8, 128)    81920       ['conv4_block13_0_relu[0][0]']   
                                                                                                  
 conv4_block13_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block13_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block13_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block13_1_relu[0][0]']   
                                                                                                  
 conv4_block13_concat (Concaten  (None, 8, 8, 672)   0           ['conv4_block12_concat[0][0]',   
 ate)                                                             'conv4_block13_2_conv[0][0]']   
                                                                                                  
 conv4_block14_0_bn (BatchNorma  (None, 8, 8, 672)   2688        ['conv4_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_0_relu (Activati  (None, 8, 8, 672)   0           ['conv4_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_1_conv (Conv2D)  (None, 8, 8, 128)    86016       ['conv4_block14_0_relu[0][0]']   
                                                                                                  
 conv4_block14_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block14_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block14_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block14_1_relu[0][0]']   
                                                                                                  
 conv4_block14_concat (Concaten  (None, 8, 8, 704)   0           ['conv4_block13_concat[0][0]',   
 ate)                                                             'conv4_block14_2_conv[0][0]']   
                                                                                                  
 conv4_block15_0_bn (BatchNorma  (None, 8, 8, 704)   2816        ['conv4_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_0_relu (Activati  (None, 8, 8, 704)   0           ['conv4_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_1_conv (Conv2D)  (None, 8, 8, 128)    90112       ['conv4_block15_0_relu[0][0]']   
                                                                                                  
 conv4_block15_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block15_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block15_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block15_1_relu[0][0]']   
                                                                                                  
 conv4_block15_concat (Concaten  (None, 8, 8, 736)   0           ['conv4_block14_concat[0][0]',   
 ate)                                                             'conv4_block15_2_conv[0][0]']   
                                                                                                  
 conv4_block16_0_bn (BatchNorma  (None, 8, 8, 736)   2944        ['conv4_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_0_relu (Activati  (None, 8, 8, 736)   0           ['conv4_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_1_conv (Conv2D)  (None, 8, 8, 128)    94208       ['conv4_block16_0_relu[0][0]']   
                                                                                                  
 conv4_block16_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block16_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block16_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block16_1_relu[0][0]']   
                                                                                                  
 conv4_block16_concat (Concaten  (None, 8, 8, 768)   0           ['conv4_block15_concat[0][0]',   
 ate)                                                             'conv4_block16_2_conv[0][0]']   
                                                                                                  
 conv4_block17_0_bn (BatchNorma  (None, 8, 8, 768)   3072        ['conv4_block16_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_0_relu (Activati  (None, 8, 8, 768)   0           ['conv4_block17_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_1_conv (Conv2D)  (None, 8, 8, 128)    98304       ['conv4_block17_0_relu[0][0]']   
                                                                                                  
 conv4_block17_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block17_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block17_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block17_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block17_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block17_1_relu[0][0]']   
                                                                                                  
 conv4_block17_concat (Concaten  (None, 8, 8, 800)   0           ['conv4_block16_concat[0][0]',   
 ate)                                                             'conv4_block17_2_conv[0][0]']   
                                                                                                  
 conv4_block18_0_bn (BatchNorma  (None, 8, 8, 800)   3200        ['conv4_block17_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_0_relu (Activati  (None, 8, 8, 800)   0           ['conv4_block18_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_1_conv (Conv2D)  (None, 8, 8, 128)    102400      ['conv4_block18_0_relu[0][0]']   
                                                                                                  
 conv4_block18_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block18_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block18_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block18_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block18_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block18_1_relu[0][0]']   
                                                                                                  
 conv4_block18_concat (Concaten  (None, 8, 8, 832)   0           ['conv4_block17_concat[0][0]',   
 ate)                                                             'conv4_block18_2_conv[0][0]']   
                                                                                                  
 conv4_block19_0_bn (BatchNorma  (None, 8, 8, 832)   3328        ['conv4_block18_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_0_relu (Activati  (None, 8, 8, 832)   0           ['conv4_block19_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_1_conv (Conv2D)  (None, 8, 8, 128)    106496      ['conv4_block19_0_relu[0][0]']   
                                                                                                  
 conv4_block19_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block19_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block19_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block19_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block19_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block19_1_relu[0][0]']   
                                                                                                  
 conv4_block19_concat (Concaten  (None, 8, 8, 864)   0           ['conv4_block18_concat[0][0]',   
 ate)                                                             'conv4_block19_2_conv[0][0]']   
                                                                                                  
 conv4_block20_0_bn (BatchNorma  (None, 8, 8, 864)   3456        ['conv4_block19_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_0_relu (Activati  (None, 8, 8, 864)   0           ['conv4_block20_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_1_conv (Conv2D)  (None, 8, 8, 128)    110592      ['conv4_block20_0_relu[0][0]']   
                                                                                                  
 conv4_block20_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block20_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block20_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block20_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block20_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block20_1_relu[0][0]']   
                                                                                                  
 conv4_block20_concat (Concaten  (None, 8, 8, 896)   0           ['conv4_block19_concat[0][0]',   
 ate)                                                             'conv4_block20_2_conv[0][0]']   
                                                                                                  
 conv4_block21_0_bn (BatchNorma  (None, 8, 8, 896)   3584        ['conv4_block20_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_0_relu (Activati  (None, 8, 8, 896)   0           ['conv4_block21_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_1_conv (Conv2D)  (None, 8, 8, 128)    114688      ['conv4_block21_0_relu[0][0]']   
                                                                                                  
 conv4_block21_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block21_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block21_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block21_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block21_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block21_1_relu[0][0]']   
                                                                                                  
 conv4_block21_concat (Concaten  (None, 8, 8, 928)   0           ['conv4_block20_concat[0][0]',   
 ate)                                                             'conv4_block21_2_conv[0][0]']   
                                                                                                  
 conv4_block22_0_bn (BatchNorma  (None, 8, 8, 928)   3712        ['conv4_block21_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_0_relu (Activati  (None, 8, 8, 928)   0           ['conv4_block22_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_1_conv (Conv2D)  (None, 8, 8, 128)    118784      ['conv4_block22_0_relu[0][0]']   
                                                                                                  
 conv4_block22_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block22_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block22_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block22_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block22_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block22_1_relu[0][0]']   
                                                                                                  
 conv4_block22_concat (Concaten  (None, 8, 8, 960)   0           ['conv4_block21_concat[0][0]',   
 ate)                                                             'conv4_block22_2_conv[0][0]']   
                                                                                                  
 conv4_block23_0_bn (BatchNorma  (None, 8, 8, 960)   3840        ['conv4_block22_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_0_relu (Activati  (None, 8, 8, 960)   0           ['conv4_block23_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_1_conv (Conv2D)  (None, 8, 8, 128)    122880      ['conv4_block23_0_relu[0][0]']   
                                                                                                  
 conv4_block23_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block23_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block23_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block23_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block23_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block23_1_relu[0][0]']   
                                                                                                  
 conv4_block23_concat (Concaten  (None, 8, 8, 992)   0           ['conv4_block22_concat[0][0]',   
 ate)                                                             'conv4_block23_2_conv[0][0]']   
                                                                                                  
 conv4_block24_0_bn (BatchNorma  (None, 8, 8, 992)   3968        ['conv4_block23_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_0_relu (Activati  (None, 8, 8, 992)   0           ['conv4_block24_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_1_conv (Conv2D)  (None, 8, 8, 128)    126976      ['conv4_block24_0_relu[0][0]']   
                                                                                                  
 conv4_block24_1_bn (BatchNorma  (None, 8, 8, 128)   512         ['conv4_block24_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv4_block24_1_relu (Activati  (None, 8, 8, 128)   0           ['conv4_block24_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv4_block24_2_conv (Conv2D)  (None, 8, 8, 32)     36864       ['conv4_block24_1_relu[0][0]']   
                                                                                                  
 conv4_block24_concat (Concaten  (None, 8, 8, 1024)  0           ['conv4_block23_concat[0][0]',   
 ate)                                                             'conv4_block24_2_conv[0][0]']   
                                                                                                  
 pool4_bn (BatchNormalization)  (None, 8, 8, 1024)   4096        ['conv4_block24_concat[0][0]']   
                                                                                                  
 pool4_relu (Activation)        (None, 8, 8, 1024)   0           ['pool4_bn[0][0]']               
                                                                                                  
 pool4_conv (Conv2D)            (None, 8, 8, 512)    524288      ['pool4_relu[0][0]']             
                                                                                                  
 pool4_pool (AveragePooling2D)  (None, 4, 4, 512)    0           ['pool4_conv[0][0]']             
                                                                                                  
 conv5_block1_0_bn (BatchNormal  (None, 4, 4, 512)   2048        ['pool4_pool[0][0]']             
 ization)                                                                                         
                                                                                                  
 conv5_block1_0_relu (Activatio  (None, 4, 4, 512)   0           ['conv5_block1_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_1_conv (Conv2D)   (None, 4, 4, 128)    65536       ['conv5_block1_0_relu[0][0]']    
                                                                                                  
 conv5_block1_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block1_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block1_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block1_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block1_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block1_1_relu[0][0]']    
                                                                                                  
 conv5_block1_concat (Concatena  (None, 4, 4, 544)   0           ['pool4_pool[0][0]',             
 te)                                                              'conv5_block1_2_conv[0][0]']    
                                                                                                  
 conv5_block2_0_bn (BatchNormal  (None, 4, 4, 544)   2176        ['conv5_block1_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_0_relu (Activatio  (None, 4, 4, 544)   0           ['conv5_block2_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_1_conv (Conv2D)   (None, 4, 4, 128)    69632       ['conv5_block2_0_relu[0][0]']    
                                                                                                  
 conv5_block2_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block2_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block2_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block2_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block2_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block2_1_relu[0][0]']    
                                                                                                  
 conv5_block2_concat (Concatena  (None, 4, 4, 576)   0           ['conv5_block1_concat[0][0]',    
 te)                                                              'conv5_block2_2_conv[0][0]']    
                                                                                                  
 conv5_block3_0_bn (BatchNormal  (None, 4, 4, 576)   2304        ['conv5_block2_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_0_relu (Activatio  (None, 4, 4, 576)   0           ['conv5_block3_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_1_conv (Conv2D)   (None, 4, 4, 128)    73728       ['conv5_block3_0_relu[0][0]']    
                                                                                                  
 conv5_block3_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block3_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block3_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block3_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block3_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block3_1_relu[0][0]']    
                                                                                                  
 conv5_block3_concat (Concatena  (None, 4, 4, 608)   0           ['conv5_block2_concat[0][0]',    
 te)                                                              'conv5_block3_2_conv[0][0]']    
                                                                                                  
 conv5_block4_0_bn (BatchNormal  (None, 4, 4, 608)   2432        ['conv5_block3_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_0_relu (Activatio  (None, 4, 4, 608)   0           ['conv5_block4_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_1_conv (Conv2D)   (None, 4, 4, 128)    77824       ['conv5_block4_0_relu[0][0]']    
                                                                                                  
 conv5_block4_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block4_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block4_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block4_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block4_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block4_1_relu[0][0]']    
                                                                                                  
 conv5_block4_concat (Concatena  (None, 4, 4, 640)   0           ['conv5_block3_concat[0][0]',    
 te)                                                              'conv5_block4_2_conv[0][0]']    
                                                                                                  
 conv5_block5_0_bn (BatchNormal  (None, 4, 4, 640)   2560        ['conv5_block4_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_0_relu (Activatio  (None, 4, 4, 640)   0           ['conv5_block5_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_1_conv (Conv2D)   (None, 4, 4, 128)    81920       ['conv5_block5_0_relu[0][0]']    
                                                                                                  
 conv5_block5_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block5_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block5_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block5_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block5_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block5_1_relu[0][0]']    
                                                                                                  
 conv5_block5_concat (Concatena  (None, 4, 4, 672)   0           ['conv5_block4_concat[0][0]',    
 te)                                                              'conv5_block5_2_conv[0][0]']    
                                                                                                  
 conv5_block6_0_bn (BatchNormal  (None, 4, 4, 672)   2688        ['conv5_block5_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_0_relu (Activatio  (None, 4, 4, 672)   0           ['conv5_block6_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_1_conv (Conv2D)   (None, 4, 4, 128)    86016       ['conv5_block6_0_relu[0][0]']    
                                                                                                  
 conv5_block6_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block6_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block6_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block6_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block6_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block6_1_relu[0][0]']    
                                                                                                  
 conv5_block6_concat (Concatena  (None, 4, 4, 704)   0           ['conv5_block5_concat[0][0]',    
 te)                                                              'conv5_block6_2_conv[0][0]']    
                                                                                                  
 conv5_block7_0_bn (BatchNormal  (None, 4, 4, 704)   2816        ['conv5_block6_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_0_relu (Activatio  (None, 4, 4, 704)   0           ['conv5_block7_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_1_conv (Conv2D)   (None, 4, 4, 128)    90112       ['conv5_block7_0_relu[0][0]']    
                                                                                                  
 conv5_block7_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block7_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block7_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block7_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block7_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block7_1_relu[0][0]']    
                                                                                                  
 conv5_block7_concat (Concatena  (None, 4, 4, 736)   0           ['conv5_block6_concat[0][0]',    
 te)                                                              'conv5_block7_2_conv[0][0]']    
                                                                                                  
 conv5_block8_0_bn (BatchNormal  (None, 4, 4, 736)   2944        ['conv5_block7_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_0_relu (Activatio  (None, 4, 4, 736)   0           ['conv5_block8_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_1_conv (Conv2D)   (None, 4, 4, 128)    94208       ['conv5_block8_0_relu[0][0]']    
                                                                                                  
 conv5_block8_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block8_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block8_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block8_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block8_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block8_1_relu[0][0]']    
                                                                                                  
 conv5_block8_concat (Concatena  (None, 4, 4, 768)   0           ['conv5_block7_concat[0][0]',    
 te)                                                              'conv5_block8_2_conv[0][0]']    
                                                                                                  
 conv5_block9_0_bn (BatchNormal  (None, 4, 4, 768)   3072        ['conv5_block8_concat[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_0_relu (Activatio  (None, 4, 4, 768)   0           ['conv5_block9_0_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_1_conv (Conv2D)   (None, 4, 4, 128)    98304       ['conv5_block9_0_relu[0][0]']    
                                                                                                  
 conv5_block9_1_bn (BatchNormal  (None, 4, 4, 128)   512         ['conv5_block9_1_conv[0][0]']    
 ization)                                                                                         
                                                                                                  
 conv5_block9_1_relu (Activatio  (None, 4, 4, 128)   0           ['conv5_block9_1_bn[0][0]']      
 n)                                                                                               
                                                                                                  
 conv5_block9_2_conv (Conv2D)   (None, 4, 4, 32)     36864       ['conv5_block9_1_relu[0][0]']    
                                                                                                  
 conv5_block9_concat (Concatena  (None, 4, 4, 800)   0           ['conv5_block8_concat[0][0]',    
 te)                                                              'conv5_block9_2_conv[0][0]']    
                                                                                                  
 conv5_block10_0_bn (BatchNorma  (None, 4, 4, 800)   3200        ['conv5_block9_concat[0][0]']    
 lization)                                                                                        
                                                                                                  
 conv5_block10_0_relu (Activati  (None, 4, 4, 800)   0           ['conv5_block10_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_1_conv (Conv2D)  (None, 4, 4, 128)    102400      ['conv5_block10_0_relu[0][0]']   
                                                                                                  
 conv5_block10_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block10_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block10_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block10_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block10_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block10_1_relu[0][0]']   
                                                                                                  
 conv5_block10_concat (Concaten  (None, 4, 4, 832)   0           ['conv5_block9_concat[0][0]',    
 ate)                                                             'conv5_block10_2_conv[0][0]']   
                                                                                                  
 conv5_block11_0_bn (BatchNorma  (None, 4, 4, 832)   3328        ['conv5_block10_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_0_relu (Activati  (None, 4, 4, 832)   0           ['conv5_block11_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_1_conv (Conv2D)  (None, 4, 4, 128)    106496      ['conv5_block11_0_relu[0][0]']   
                                                                                                  
 conv5_block11_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block11_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block11_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block11_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block11_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block11_1_relu[0][0]']   
                                                                                                  
 conv5_block11_concat (Concaten  (None, 4, 4, 864)   0           ['conv5_block10_concat[0][0]',   
 ate)                                                             'conv5_block11_2_conv[0][0]']   
                                                                                                  
 conv5_block12_0_bn (BatchNorma  (None, 4, 4, 864)   3456        ['conv5_block11_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_0_relu (Activati  (None, 4, 4, 864)   0           ['conv5_block12_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_1_conv (Conv2D)  (None, 4, 4, 128)    110592      ['conv5_block12_0_relu[0][0]']   
                                                                                                  
 conv5_block12_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block12_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block12_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block12_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block12_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block12_1_relu[0][0]']   
                                                                                                  
 conv5_block12_concat (Concaten  (None, 4, 4, 896)   0           ['conv5_block11_concat[0][0]',   
 ate)                                                             'conv5_block12_2_conv[0][0]']   
                                                                                                  
 conv5_block13_0_bn (BatchNorma  (None, 4, 4, 896)   3584        ['conv5_block12_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_0_relu (Activati  (None, 4, 4, 896)   0           ['conv5_block13_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_1_conv (Conv2D)  (None, 4, 4, 128)    114688      ['conv5_block13_0_relu[0][0]']   
                                                                                                  
 conv5_block13_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block13_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block13_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block13_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block13_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block13_1_relu[0][0]']   
                                                                                                  
 conv5_block13_concat (Concaten  (None, 4, 4, 928)   0           ['conv5_block12_concat[0][0]',   
 ate)                                                             'conv5_block13_2_conv[0][0]']   
                                                                                                  
 conv5_block14_0_bn (BatchNorma  (None, 4, 4, 928)   3712        ['conv5_block13_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_0_relu (Activati  (None, 4, 4, 928)   0           ['conv5_block14_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_1_conv (Conv2D)  (None, 4, 4, 128)    118784      ['conv5_block14_0_relu[0][0]']   
                                                                                                  
 conv5_block14_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block14_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block14_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block14_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block14_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block14_1_relu[0][0]']   
                                                                                                  
 conv5_block14_concat (Concaten  (None, 4, 4, 960)   0           ['conv5_block13_concat[0][0]',   
 ate)                                                             'conv5_block14_2_conv[0][0]']   
                                                                                                  
 conv5_block15_0_bn (BatchNorma  (None, 4, 4, 960)   3840        ['conv5_block14_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_0_relu (Activati  (None, 4, 4, 960)   0           ['conv5_block15_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_1_conv (Conv2D)  (None, 4, 4, 128)    122880      ['conv5_block15_0_relu[0][0]']   
                                                                                                  
 conv5_block15_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block15_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block15_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block15_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block15_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block15_1_relu[0][0]']   
                                                                                                  
 conv5_block15_concat (Concaten  (None, 4, 4, 992)   0           ['conv5_block14_concat[0][0]',   
 ate)                                                             'conv5_block15_2_conv[0][0]']   
                                                                                                  
 conv5_block16_0_bn (BatchNorma  (None, 4, 4, 992)   3968        ['conv5_block15_concat[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_0_relu (Activati  (None, 4, 4, 992)   0           ['conv5_block16_0_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_1_conv (Conv2D)  (None, 4, 4, 128)    126976      ['conv5_block16_0_relu[0][0]']   
                                                                                                  
 conv5_block16_1_bn (BatchNorma  (None, 4, 4, 128)   512         ['conv5_block16_1_conv[0][0]']   
 lization)                                                                                        
                                                                                                  
 conv5_block16_1_relu (Activati  (None, 4, 4, 128)   0           ['conv5_block16_1_bn[0][0]']     
 on)                                                                                              
                                                                                                  
 conv5_block16_2_conv (Conv2D)  (None, 4, 4, 32)     36864       ['conv5_block16_1_relu[0][0]']   
                                                                                                  
 conv5_block16_concat (Concaten  (None, 4, 4, 1024)  0           ['conv5_block15_concat[0][0]',   
 ate)                                                             'conv5_block16_2_conv[0][0]']   
                                                                                                  
 bn (BatchNormalization)        (None, 4, 4, 1024)   4096        ['conv5_block16_concat[0][0]']   
                                                                                                  
 relu (Activation)              (None, 4, 4, 1024)   0           ['bn[0][0]']                     
                                                                                                  
 avg_pool (GlobalAveragePooling  (None, 1024)        0           ['relu[0][0]']                   
 2D)                                                                                              
                                                                                                  
==================================================================================================
Total params: 7,037,504
Trainable params: 6,953,856
Non-trainable params: 83,648
__________________________________________________________________________________________________


layers = base_model.layers
print(f"The model has {len(layers)} layers")

The model has 428 layers


print(f"The input shape {base_model.input}")
print(f"The output shape {base_model.output}")

The input shape KerasTensor(type_spec=TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name='input_4'), name='input_4', description="created by layer 'input_4'")
The output shape KerasTensor(type_spec=TensorSpec(shape=(None, 1024), dtype=tf.float32, name=None), name='avg_pool/Mean:0', description="created by layer 'avg_pool'")


#model = Sequential()
base_model = DenseNet121(include_top=False, weights='imagenet')
x = base_model.output

x = GlobalAveragePooling2D()(x)

predictions = Dense(1, activation="sigmoid")(x)

model7 = Model(inputs=base_model.input, outputs=predictions)
#model7.add(base_model)
#model7.add(GlobalAveragePooling2D())
#model7.add(Dense(1, activation='sigmoid'))

model7.compile(loss='binary_crossentropy', 
              optimizer='adam', 
              metrics=['accuracy'])


#history7 = model7.fit(X_train, 
#                    y_train,
#                    epochs=10,
#                    validation_data=valid_generator,
#                    batch_size = batch_size
#                        )

history7 = model7.fit(
    X_train, 
    y_train,
    epochs=epochs,
    batch_size = batch_size,
    validation_data=valid_generator
)

Epoch 1/30

2022-08-26 15:31:38.934348: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - ETA: 0s - loss: 0.4382 - accuracy: 0.7992

2022-08-26 15:33:45.929377: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

182/182 [==============================] - 148s 748ms/step - loss: 0.4382 - accuracy: 0.7992 - val_loss: 0.4678 - val_accuracy: 0.7961
Epoch 2/30
182/182 [==============================] - 127s 693ms/step - loss: 0.3910 - accuracy: 0.8216 - val_loss: 0.4785 - val_accuracy: 0.8103
Epoch 3/30
182/182 [==============================] - 126s 691ms/step - loss: 0.3760 - accuracy: 0.8282 - val_loss: 0.4846 - val_accuracy: 0.8078
Epoch 4/30
182/182 [==============================] - 125s 687ms/step - loss: 0.3640 - accuracy: 0.8347 - val_loss: 0.4362 - val_accuracy: 0.7939
Epoch 5/30
182/182 [==============================] - 126s 690ms/step - loss: 0.3506 - accuracy: 0.8387 - val_loss: 2.4647 - val_accuracy: 0.5275
Epoch 6/30
182/182 [==============================] - 126s 691ms/step - loss: 0.3277 - accuracy: 0.8550 - val_loss: 0.5198 - val_accuracy: 0.7413
Epoch 7/30
182/182 [==============================] - 126s 691ms/step - loss: 0.3079 - accuracy: 0.8634 - val_loss: 0.4728 - val_accuracy: 0.7757
Epoch 8/30
182/182 [==============================] - 261s 1s/step - loss: 0.2928 - accuracy: 0.8757 - val_loss: 1.4769 - val_accuracy: 0.6970
Epoch 9/30
182/182 [==============================] - 127s 698ms/step - loss: 0.2747 - accuracy: 0.8818 - val_loss: 0.4963 - val_accuracy: 0.7916
Epoch 10/30
182/182 [==============================] - 126s 691ms/step - loss: 0.2312 - accuracy: 0.9026 - val_loss: 0.7710 - val_accuracy: 0.7359
Epoch 11/30
182/182 [==============================] - 125s 688ms/step - loss: 0.2038 - accuracy: 0.9156 - val_loss: 0.9367 - val_accuracy: 0.6930
Epoch 12/30
182/182 [==============================] - 126s 690ms/step - loss: 0.1647 - accuracy: 0.9323 - val_loss: 0.4552 - val_accuracy: 0.8189
Epoch 13/30
182/182 [==============================] - 159s 871ms/step - loss: 0.1380 - accuracy: 0.9456 - val_loss: 0.8087 - val_accuracy: 0.7761
Epoch 14/30
182/182 [==============================] - 126s 691ms/step - loss: 0.1143 - accuracy: 0.9550 - val_loss: 0.4909 - val_accuracy: 0.8209
Epoch 15/30
182/182 [==============================] - 332s 2s/step - loss: 0.0963 - accuracy: 0.9631 - val_loss: 1.0531 - val_accuracy: 0.6376
Epoch 16/30
182/182 [==============================] - 125s 684ms/step - loss: 0.0849 - accuracy: 0.9672 - val_loss: 0.6720 - val_accuracy: 0.8220
Epoch 17/30
182/182 [==============================] - 461s 3s/step - loss: 0.0678 - accuracy: 0.9750 - val_loss: 0.8138 - val_accuracy: 0.8057
Epoch 18/30
182/182 [==============================] - 126s 690ms/step - loss: 0.0626 - accuracy: 0.9754 - val_loss: 0.8996 - val_accuracy: 0.8042
Epoch 19/30
182/182 [==============================] - 126s 688ms/step - loss: 0.0677 - accuracy: 0.9754 - val_loss: 0.6584 - val_accuracy: 0.8258
Epoch 20/30
182/182 [==============================] - 130s 716ms/step - loss: 0.0460 - accuracy: 0.9835 - val_loss: 0.7335 - val_accuracy: 0.8255
Epoch 21/30
182/182 [==============================] - 126s 690ms/step - loss: 0.0489 - accuracy: 0.9814 - val_loss: 0.9340 - val_accuracy: 0.8045
Epoch 22/30
182/182 [==============================] - 126s 693ms/step - loss: 0.0441 - accuracy: 0.9837 - val_loss: 0.7091 - val_accuracy: 0.8144
Epoch 23/30
182/182 [==============================] - 126s 690ms/step - loss: 0.0439 - accuracy: 0.9838 - val_loss: 0.8634 - val_accuracy: 0.8205
Epoch 24/30
182/182 [==============================] - 126s 689ms/step - loss: 0.0403 - accuracy: 0.9858 - val_loss: 1.3130 - val_accuracy: 0.7598
Epoch 25/30
182/182 [==============================] - 293s 2s/step - loss: 0.0328 - accuracy: 0.9883 - val_loss: 0.8829 - val_accuracy: 0.8184
Epoch 26/30
182/182 [==============================] - 126s 692ms/step - loss: 0.0368 - accuracy: 0.9863 - val_loss: 0.9353 - val_accuracy: 0.8387
Epoch 27/30
182/182 [==============================] - 125s 685ms/step - loss: 0.0264 - accuracy: 0.9902 - val_loss: 0.8630 - val_accuracy: 0.8210
Epoch 28/30
182/182 [==============================] - 126s 690ms/step - loss: 0.0327 - accuracy: 0.9876 - val_loss: 0.8096 - val_accuracy: 0.8358
Epoch 29/30
182/182 [==============================] - 127s 695ms/step - loss: 0.0364 - accuracy: 0.9868 - val_loss: 0.8021 - val_accuracy: 0.8159
Epoch 30/30
182/182 [==============================] - 126s 692ms/step - loss: 0.0320 - accuracy: 0.9881 - val_loss: 0.9067 - val_accuracy: 0.8141


model7_test_loss, model7_test_acc = model7.evaluate(X_test, y_test, verbose=1)
print('Test Loss of the model is -:', model7_test_loss)
print('Test Accuracy of the model is:', model7_test_acc)

189/189 [==============================] - 20s 89ms/step - loss: 0.9013 - accuracy: 0.8179
Test Loss of the model is -: 0.9012991189956665
Test Accuracy of the model is: 0.8178961277008057


display_loss_accuracy(history7)


# printing the dataframe
train7_acc = history7.history['accuracy']
resultDF7 = createResult("DenseNet_model",
                         round(train7_acc[-1]*100,2),round(model7_test_acc*100,2))
resultDF7


evaluate_model(inception_model, X_test, y_test)

189/189 [==============================] - 7s 36ms/step
----------------------Confusion Matrix---------------------


-----------------Classification Report-----------------

              precision    recall  f1-score   support

           0       0.83      0.54      0.66      1911
           1       0.82      0.95      0.88      4135

    accuracy                           0.82      6046
   macro avg       0.83      0.74      0.77      6046
weighted avg       0.82      0.82      0.81      6046


-----------------------ROC Curve-----------------------


# Predict from the test set
threshold = 0.5
# Predict from the test set
y_prob7 = model7.predict(x=X_test)
y_prob7 = y_prob7.reshape((len(y_prob7),))

# Convert to binary probabilities
y_hat7   = y_prob7 > threshold
y_hat7   = y_hat7.reshape(len(y_hat7),)

    
reportData = classification_report(y_test, y_hat7,output_dict=True)

for data in reportData:
    if(data == '-1' or data == '1'):
        if(type(reportData[data]) is dict):
            for subData in reportData[data]:
                resultDF7[data+"_"+subData] = reportData[data][subData]

resultDF7['1_precision'] =round(resultDF7['1_precision']*100,2)
resultDF7['1_recall'] =round(resultDF7['1_recall']*100,2)
resultDF7['1_f1-score'] =round(resultDF7['1_f1-score']*100,2)
resultDF7

2022-08-26 16:52:00.791544: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

189/189 [==============================] - 17s 80ms/step


df =pd.concat([resultDF1,resultDF2,resultDF3,resultDF4,resultDF5,resultDF6,resultDF7],ignore_index=True)
df


test_df =  pd.read_csv('stage_2_sample_submission.csv')
test_df


images = []
ADJUSTED_IMAGE_SIZE = 128
imageList = []
classLabels = []
labels = []
originalImage = []
# Function to read the image from the path and reshape the image to size
def readAndReshapeImage(image):
    img = np.array(image).astype(np.uint8)
    ## Resize the image
    res = cv2.resize(img,(ADJUSTED_IMAGE_SIZE,ADJUSTED_IMAGE_SIZE), interpolation = cv2.INTER_LINEAR)
    return res

## Read the imahge and resize the image
def populateImage(rowData):
    for index, row in rowData.iterrows():
        patientId = row.patientId
       # classlabel = row["Target"]
        dcm_file = 'stage_2_test_images/'+'{}.dcm'.format(patientId)
        dcm_data = dcm.read_file(dcm_file)
        img = dcm_data.pixel_array
        ## Converting the image to 3 channels as the dicom image pixel does not have colour classes with it
        if len(img.shape) != 3 or img.shape[2] != 3:
            img = np.stack((img,) * 3, -1)
        imageList.append(readAndReshapeImage(img))
#         originalImage.append(img)
        #classLabels.append(classlabel)
    tmpImages = np.array(imageList)
    #tmpLabels = np.array(classLabels)
#     originalImages = np.array(originalImage)
    return tmpImages


test_images = populateImage(test_df)


test_images.shape

(3000, 128, 128, 3)


## Checking one of the converted image 
plt.imshow(test_images[1200])

<matplotlib.image.AxesImage at 0x81d48aca0>


test_gen  = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)
  
test_generator = test_gen.flow(test_images)


predictions = (model1.predict(test_generator) > 0.5).astype("int32")
predictions

2022-08-26 17:00:27.989684: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

94/94 [==============================] - 1s 11ms/step

array([[1],
       [0],
       [1],
       ...,
       [1],
       [1],
       [0]], dtype=int32)


inception_model.save('best_inception_model_final_26_aug.h5')


# Insights of transfer learning and final conclusion:

# Among the CNN model with 2 classes (clubbed the classes of no lung opacity and normal) 
# and CNN model with 3 classes using stratify sampling (inorder to make sure of the same ratio of classes
# for modelling), it has been found that accuracy and recall of pnemonia class (class 1) are higher
# for model with 2 classes, so we have used 2 classes only for further analysis/modelling 

# then we have tried the CNN model (with 2 classes) after augumenting the images and this model has shown 
# slightly increase in recall percentage with relatively no change in test accuracy inspite of slight 
# decrease in train accuracy

# CNN_VGG16_model with trainable -True - 
# Later on we have tried with transfer learning models of VGG 16. Among the trainable- false and true,
# it has been observed that VGG 16 trainable -False has shown relatively higher accuracy when compared
# to VGG trainable- True.

# Further we have tried with inception net (Google net) and dense net. Dense net has given highest training
# accuracy in train and test much difference that mean overfitting
# among all the techniques but quites high as well recall percentage similary to inception net

# Finally, it has been found that inception net is the best among all transfer learning models being tried 
# and has given highest recall percentage which is the main moto of this project since this belongs 
# to health care domain and need to focus on decreasing the false negative rate

	patientId	x	y	width	height	Target
5	00436515-870c-4b36-a041-de91049b9ab4	562.0	152.0	256.0	453.0	1
9	00704310-78a8-4b38-8475-49f4573b2dbb	695.0	575.0	162.0	137.0	1
15	00aecb01-a116-45a2-956c-08d2fa55433f	547.0	299.0	119.0	165.0	1
17	00c0b293-48e7-4e16-ac76-9269ba535a62	650.0	511.0	206.0	284.0	1
20	00f08de1-517e-4652-a04f-d1dc9ee48593	571.0	275.0	230.0	476.0	1

	patientId	x	y	width	height	Target
8	00704310-78a8-4b38-8475-49f4573b2dbb	323.0	577.0	160.0	104.0	1
9	00704310-78a8-4b38-8475-49f4573b2dbb	695.0	575.0	162.0	137.0	1

	patientId	number_of_boxes
0	000db696-cf54-4385-b10b-6b16fbb3f985	2
1	000fe35a-2649-43d4-b027-e67796d412e0	2
2	001031d9-f904-4a23-b3e5-2c088acd19c6	2
3	001916b8-3d30-4935-a5d1-8eaddb1646cd	1
4	0022073f-cec8-42ec-ab5f-bc2314649235	2
...	...	...
6007	ffa424d2-6e6b-4eed-93ab-7551e8941215	2
6008	ffae40ab-fcfe-4311-a74a-89f605dba48b	1
6009	ffd787b6-59ca-48cb-bd15-bcedd52cf37c	2
6010	fff0b503-72a5-446a-843d-f3d152e39053	1
6011	fffb2395-8edd-4954-8a89-ffe2fd329be3	2

	patientId	x	y	width	height	Target	class
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0	No Lung Opacity / Not Normal
28989	000924cf-0f8d-42bd-9158-1af53881a557	NaN	NaN	NaN	NaN	0	Normal
28990	000db696-cf54-4385-b10b-6b16fbb3f985	316.0	318.0	170.0	478.0	1	Lung Opacity
28991	000db696-cf54-4385-b10b-6b16fbb3f985	660.0	375.0	146.0	402.0	1	Lung Opacity
28992	000fe35a-2649-43d4-b027-e67796d412e0	570.0	282.0	269.0	409.0	1	Lung Opacity

	Method	Accuracy	Test Score	1_precision	1_recall	1_f1-score	1_support
0	CNN model with 2 classes	83.45	81.34	84.83	88.56	86.65	4135
1	CNN model with 3 classes	70.06	64.80	60.74	48.29	53.80	2365
2	CNN model-1 with Augumentation	79.72	80.28	84.52	87.13	85.81	4135

AIML Online Capstone - Pneumonia Detection Challenge:¶

Insights of info:¶

insights of null values:¶

Reading the Dicom images meta data and appending it to the training set¶

Observations of age:¶

insights:¶

insights:¶

insights:¶

Insights:¶

Insight:¶

Insight:¶

insight:¶

Model-1 with 2 classes¶

Model-2 with 3 classes¶

Model-1 with Data Augumentation(2 classes)¶

Data Augmentation¶

Transfer Learning¶

Modeling - Using VGG16 with trainable = False¶

Modeling - Using VGG16 with trainable = True¶

InceptionNet¶

DenseNet¶

	patientId	x	y	width	height	Target
0	0004cfab-14fd-4e49-80ba-63a80b6bddd6	NaN	NaN	NaN	NaN	0
1	00313ee0-9eaa-42f4-b0ab-c148ed3241cd	NaN	NaN	NaN	NaN	0
2	00322d4d-1c29-4943-afc9-b6754be640eb	NaN	NaN	NaN	NaN	0
3	003d8fa0-6bf1-40ed-b54c-ac657f8495c5	NaN	NaN	NaN	NaN	0
4	00436515-870c-4b36-a041-de91049b9ab4	264.0	152.0	213.0	379.0	1

	patientId	x	y	width	height	Target
16	00c0b293-48e7-4e16-ac76-9269ba535a62	306.0	544.0	168.0	244.0	1
17	00c0b293-48e7-4e16-ac76-9269ba535a62	650.0	511.0	206.0	284.0	1

	patientId	x	y	width	height	Target	class	sex	age
count	30227	9555.000000	9555.000000	9555.000000	9555.000000	30227.000000	30227	30227	30227.000000
unique	26684	NaN	NaN	NaN	NaN	NaN	3	2	NaN
top	76f71a93-8105-4c79-a010-0cfa86f0061a	NaN	NaN	NaN	NaN	NaN	No Lung Opacity / Not Normal	M	NaN
freq	4	NaN	NaN	NaN	NaN	NaN	11821	17216	NaN
mean	NaN	394.047724	366.839560	218.471376	329.269702	0.316108	NaN	NaN	46.797764
std	NaN	204.574172	148.940488	59.289475	157.750755	0.464963	NaN	NaN	16.892940
min	NaN	2.000000	2.000000	40.000000	45.000000	0.000000	NaN	NaN	1.000000
25%	NaN	207.000000	249.000000	177.000000	203.000000	0.000000	NaN	NaN	34.000000
50%	NaN	324.000000	365.000000	217.000000	298.000000	0.000000	NaN	NaN	49.000000
75%	NaN	594.000000	478.500000	259.000000	438.000000	1.000000	NaN	NaN	59.000000
max	NaN	835.000000	881.000000	528.000000	942.000000	1.000000	NaN	NaN	155.000000

	patientId	PredictionString
0	0000a175-0e68-4ca4-b1af-167204a7e0bc	0.5 0 0 100 100
1	0005d3cc-3c3f-40b9-93c3-46231c3eb813	0.5 0 0 100 100
2	000686d7-f4fc-448d-97a0-44fa9c5d3aa6	0.5 0 0 100 100
3	000e3a7d-c0ca-4349-bb26-5af2d8993c3d	0.5 0 0 100 100
4	00100a24-854d-423d-a092-edcf6179e061	0.5 0 0 100 100
...	...	...
2995	c1e88810-9e4e-4f39-9306-8d314bfc1ff1	0.5 0 0 100 100
2996	c1ec035b-377b-416c-a281-f868b7c9b6c3	0.5 0 0 100 100
2997	c1ef5b66-0fd7-49d1-ae6b-5af84929414b	0.5 0 0 100 100
2998	c1ef6724-f95f-40f1-b25b-de806d9bc39d	0.5 0 0 100 100
2999	c1f55e7e-4065-4dc0-993e-a7c1704c6036	0.5 0 0 100 100

	number_of_patientIDs_per_boxes
number_of_boxes
1	2614
2	3266
3	119
4	13